A vehicle cabin monitoring system includes a customization profile for storing: annotated images associated with annotation(s) indicating a ground truth for an associated region of an image; and a plurality of core processing parameters for an image processing component of an image processor. The system is: responsive to user interaction with a user interactive control of the vehicle within a field of view of a camera for storing an image acquired by the camera at the time of interaction in the customization profile with an annotation indicating a ground truth for an associated region of the image according to the interaction; and configured to use images from the customization profile for re-training an image processing component of the processor and for storing updated core processing parameters produced by the re-training in the customization profile for use by the re-trained image processing component in processing subsequently acquired images.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/10 - Corps d’êtres humains ou d’animaux, p.ex. occupants de véhicules automobiles ou piétons; Parties du corps, p.ex. mains
2.
Method And System For Camera Motion Blur Reduction
A method for reducing camera motion blur comprises, before acquiring an image frame for a video stream, a camera measurement unit measuring data related to a camera module motion during a time window; determining camera module motion based on the measured data and predicting a camera motion blur during acquisition of the image frame based at least on the determined camera module motion and the lens projection model; determining whether the predicted camera motion blur exceeds a threshold; in response to determining that the predicted camera motion blur exceeds the threshold, determining a reduction of the provisional exposure time determined to acquire the image frame so that the predicted camera motion blur reaches the threshold, determining whether a corresponding increase in the provisional gain determined to acquire the image frame is below a maximum gain value, adjusting the provisional exposure time and gain, and acquiring the image frame.
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
3.
Methods And Systems to Predict Activity In A Sequence Of Images
A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.
G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
G06V 20/30 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p.ex. des photos ou des vidéos issus des réseaux sociaux
G06F 18/22 - Critères d'appariement, p.ex. mesures de proximité
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
G06F 18/2113 - Sélection du sous-ensemble de caractéristiques le plus significatif en classant ou en filtrant l'ensemble des caractéristiques, p.ex. en utilisant une mesure de la variance ou de la corrélation croisée des caractéristiques
Disclosed is a multi-modal convolutional neural network (CNN) for fusing image information from a frame based camera, such as, a near infra-red (NIR) camera and an event camera for analysing facial characteristics in order to produce classifications such as head pose or eye gaze. The neural network processes image frames acquired from each camera through a plurality of convolutional layers to provide a respective set of one or more intermediate images. The network fuses at least one corresponding pair of intermediate images generated from each of image frames through an array of fusing cells. Each fusing cell is connected to at least a respective element of each intermediate image and is trained to weight each element from each intermediate image to provide the fused output. The neural network further comprises at least one task network configured to generate one or more task outputs for the region of interest.
A method at a first participant's client conferencing system in a videoconference comprises receiving, from a second client conferencing system, at least one first video frame of a first video signal including an image of the second participant looking at a third participant, and first metadata associated with the first video frame and including an identity of the third participant. The image of the second participant is modified in the first video frame so that the first video frame is displayed on a first area of the client conferencing system with the second participant looking at a second area of the first display configured for displaying a second video signal of the third participant identified by the first metadata.
A decompression apparatus comprises a number of stages including: a first stage which always reads a binary symbol from a first stage indicator file for each symbol which is to be decoded; one or more mid stages which conditionally read a binary symbol from successive indicator files based on the value of the last symbol read from a previous indicator file; and a final stage which conditionally reads a symbol from a reduced file based on the value of the last symbol read from the last stage indicator file.
G06F 16/174 - Systèmes de fichiers; Serveurs de fichiers - Détails d’autres fonctions de systèmes de fichiers Élimination de redondances par le système de fichiers
H03M 7/30 - Compression; Expansion; Elimination de données inutiles, p.ex. réduction de redondance
An auto-exposure module determines a first set of exposure parameters to acquire a first image. The first image is acquired using the first set of exposure parameters. The module determines a target image histogram; calculates an image histogram based on intensity values of the pixels of the first image; determines an integral of the image histogram and of the target image histogram for a range of pixel intensity values; calculates a transfer curve for transforming the integral of the image histogram to match the integral of the target image histogram; calculates a slope of a line fitting at least a portion of the transfer curve; determines a correction factor based on the calculated slope; and adjusts the first set of exposure parameters according to the correction factor. A second image is then acquired using the adjusted first set of exposure parameters.
A vehicle occupant monitoring system, OMS, comprises an image acquisition device with a rolling shutter image sensor configured to selectively operate in either: a full-resolution image mode where an image frame corresponding to the full image sensor is provided; or a region of interest, ROI, mode, where an image frame corresponding to a limited portion of the image sensor is provided. An object detector is configured to receive a full-resolution image from the image sensor and to identify a ROI corresponding to an object of interest within the image. A controller is configured to obtain an image corresponding to the ROI from the image sensor operating in ROI mode, the image having an exposure time long enough for all rows of the ROI to be illuminated by a common pulse of light from at least one infra-red light source and short enough to limit motion blur within the image.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
H04N 5/33 - Transformation des rayonnements infrarouges
H04N 23/73 - Circuits de compensation de la variation de luminosité dans la scène en influençant le temps d'exposition
H04N 23/74 - Circuits de compensation de la variation de luminosité dans la scène en influençant la luminosité de la scène à l'aide de moyens d'éclairage
9.
Vehicle occupant monitoring system including an image acquisition device with a rolling shutter image sensor
A vehicle occupant monitoring system, OMS, comprises an image acquisition device with a rolling shutter image sensor comprising an array of sub-pixels which are respectively selectively sensitive to: red and infra-red; blue and infra-red; and green and infra-red light. The device is configured to selectively operate in either: a colour mode where a multi-plane image frame corresponding to the full image sensor is provided, each plane derived from red, green or blue sensitive sub-pixels respectively; or a monochrome mode, where sensor information from sub-pixels is aggregated to provide a single image plane.
H04N 23/667 - Changement de mode de fonctionnement de la caméra, p. ex. entre les modes photo et vidéo, sport et normal ou haute et basse résolutions
H04N 5/33 - Transformation des rayonnements infrarouges
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
H04N 23/73 - Circuits de compensation de la variation de luminosité dans la scène en influençant le temps d'exposition
H04N 23/74 - Circuits de compensation de la variation de luminosité dans la scène en influençant la luminosité de la scène à l'aide de moyens d'éclairage
H04N 23/76 - Circuits de compensation de la variation de luminosité dans la scène en agissant sur le signal d'image
10.
Vehicle occupant monitoring system including an image acquisition device with a rolling shutter image sensor
A vehicle occupant monitoring system, OMS, comprises: an image acquisition device comprising an image sensor and a lens assembly having a varying transmissivity across a field of view of the image sensor; at least one infra-red, IR, light source disposed within a cabin of the vehicle and being configured to illuminate at least one occupant of the vehicle with varying illumination across the field of view of the image sensor; and an image processing pipeline configured to obtain and pre-process an image acquired from the image sensor in accordance with a lens shading map and a cabin illumination map in order to compensate for both the varying transmissivity and the varying illumination in order to provide a more uniformly illuminated image to a controller for further analysis.
H04N 23/74 - Circuits de compensation de la variation de luminosité dans la scène en influençant la luminosité de la scène à l'aide de moyens d'éclairage
H04N 25/42 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner en commutant entre différents modes de fonctionnement utilisant des résolutions ou des formats d'images différents, p.ex. entre un mode d'images fixes et un mode d'images vidéo ou entre un mode entrelacé et un mode non entrelacé
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
B60R 11/04 - Montage des caméras pour fonctionner pendant la marche; Disposition de leur commande par rapport au véhicule
11.
VEHICLE OCCUPANT MONITORING SYSTEM INCLUDING AN IMAGE ACQUISITION DEVICE WITH A ROLLING SHUTTER IMAGE SENSOR
A vehicle occupant monitoring system, OMS, comprises an image acquisition device with a rolling shutter image sensor configured to selectively operate in either: a full-resolution image mode where an image frame corresponding to the full image sensor is provided; or a region of interest, ROI, mode, where an image frame corresponding to a limited portion of the image sensor is provided. An object detector is configured to receive a full-resolution image from the image sensor and to identify a ROI corresponding to an object of interest within the image. A controller is configured to obtain an image corresponding to the ROI from the image sensor operating in ROI mode, the image having an exposure time long enough for all rows of the ROI to be illuminated by a common pulse of light from at least one infra-red light source and short enough to limit motion blur within the image.
G06V 10/143 - Détection ou éclairage à des longueurs d’onde différentes
G06V 10/147 - Caractéristiques optiques de l’appareil qui effectue l’acquisition ou des dispositifs d’éclairage - Détails de capteurs, p.ex. lentilles de capteurs
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p.ex. utilisant un modèle de réflectance ou d’éclairage
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06V 10/98 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos Évaluation de la qualité des motifs acquis
12.
Method and system for camera motion blur reduction
A method for reducing camera motion blur comprises, before acquiring an image frame for a video stream, a camera measurement unit measuring data related to a camera module motion during a time window; determining camera module motion based on the measured data and predicting a camera motion blur during acquisition of the image frame based at least on the determined camera module motion and the lens projection model; determining whether the predicted camera motion blur exceeds a threshold; in response to determining that the predicted camera motion blur exceeds the threshold, determining a reduction of the provisional exposure time determined to acquire the image frame so that the predicted camera motion blur reaches the threshold, determining whether a corresponding increase in the provisional gain determined to acquire the image frame is below a maximum gain value, adjusting the provisional exposure time and gain, and acquiring the image frame.
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
An image processing system comprising a processer configured to receive a sequence of images frames from an image acquisition device and configured to: analyze at least a currently acquired image frame to determine if activity is occurring in an environment with a field of view of the image acquisition device; responsive to analyzing a subsequent image frame acquired after the currently acquired image frame and determining that no activity is occurring in the environment, retrieve an image frame acquired before the currently acquired image frame which has been analyzed and where it has been determined that no activity is occurring in the environment; analyze the subsequent image frame and the retrieved image frame to identify a state of one or more objects within the field of view of the image acquisition device; and responsive to a change in state of the one or more objects, notify a user accordingly.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
A method for calibrating a vehicle cabin camera having: a pitch, yaw and roll angle; and a field of view capturing vehicle cabin features which are symmetric about a vehicle longitudinal axis comprises: selecting points from within an image of the vehicle cabin and projecting the points onto a 3D unit circle in accordance with a camera projection model. For each of one or more rotations of a set of candidate yaw and roll rotations, the method comprises: rotating the projected points with the rotation; flipping the rotated points about a pitch axis; counter-rotating the projected points with an inverse of the rotation; and mapping the counter-rotated points back into an image plane to provide a set of transformed points. A candidate rotation which provides a best match between the set of transformed points and the locations of the selected points in the image plane is selected.
A method for identifying a gesture from one of a plurality of dynamic gestures, each dynamic gesture comprising a distinct movement made by a user over a period of time within a field of view of an image acquisition device comprises iteratively: acquiring a current image from said image acquisition device at a given time; and passing at least a portion of the current image through a bidirectionally recurrent multi-layer classifier. A final layer of the multi-layer classifier comprises an output indicating a probability that a gesture from the plurality of dynamic gestures is being made by a user during the time of acquiring the image.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/20 - Mouvements ou comportement, p.ex. reconnaissance des gestes
A method for identifying a gesture from one of a plurality of dynamic gestures, each dynamic gesture comprising a distinct movement made by a user over a period of time within a field of view of an image acquisition device comprises iteratively: acquiring a current image from said image acquisition device at a given time; and passing at least a portion of the current image through a bidirectionally recurrent multi-layer classifier. A final layer of the multi-layer classifier comprises an output indicating a probability that a gesture from the plurality of dynamic gestures is being made by a user during the time of acquiring the image.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A method at a first participant's client conferencing system in a videoconference comprises receiving, from a second client conferencing system, at least one first video frame of a first video signal including an image of the second participant looking at a third participant, and first metadata associated with the first video frame and including an identity of the third participant. The image of the second participant is modified in the first video frame so that the first video frame is displayed on a first area of the client conferencing system with the second participant looking at a second area of the first display configured for displaying a second video signal of the third participant identified by the first metadata.
A method for generating a composite image comprises: detecting a color temperature of a background image; acquiring from a camera through an image signal processor, ISP, performing white balance correction of acquired image data, an image including a foreground region including face of a user; and detecting a color temperature of the foreground region. Responsive to the color temperature for the foreground region differing from that of the background image by more than a threshold amount, a color temperature for white balance correction of a subsequently acquired image is set which causes skin pixels within the foreground region of the subsequently acquired image to have a color temperature closer to the color temperature for the background image. Pixel values of the foreground region are combined with pixel values of the background image corresponding to a background region of the acquired image to provide the composite image.
H04N 5/272 - Moyens pour insérer une image de premier plan dans une image d'arrière plan, c. à d. incrustation, effet inverse
G06T 7/194 - Découpage; Détection de bords impliquant une segmentation premier plan-arrière-plan
H04N 23/88 - Chaînes de traitement de la caméra; Leurs composants pour le traitement de signaux de couleur pour l'équilibrage des couleurs, p.ex. circuits pour équilibrer le blanc ou commande de la température de couleur
A method for monitoring occupants of a vehicle comprises identifying a respective body region for one or more occupants of the vehicle within an obtained image; identifying within the body regions, skeletal points including points on an arm of a body; identifying one or more hand regions; and determining a hand region to be either a left or a right hand of an occupant in accordance with its spatial relationship with identified skeletal points of the body region of an occupant. The left or right hand region for the occupant are provided to a pair of classifiers to provide an activity classification for the occupant, a first classifier being trained with images of hands of occupants in states where objects involved are not visible and a second classifier being trained with images of occupants in the states where the objects are visible in at least one hand region.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
B60W 40/08 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conducteurs ou aux passagers
A method for monitoring occupants of a vehicle comprises identifying a respective body region for one or more occupants of the vehicle within an obtained image; identifying within the body regions, skeletal points including points on an arm of a body; identifying one or more hand regions; and determining a hand region to be either a left or a right hand of an occupant in accordance with its spatial relationship with identified skeletal points of the body region of an occupant. The left or right hand region for the occupant are provided to a pair of classifiers to provide an activity classification for the occupant, a first classifier being trained with images of hands of occupants in states where objects involved are not visible and a second classifier being trained with images of occupants in the states where the objects are visible in at least one hand region.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06V 40/10 - Corps d’êtres humains ou d’animaux, p.ex. occupants de véhicules automobiles ou piétons; Parties du corps, p.ex. mains
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
21.
Multi-Baseline Camera Array System Architectures for Depth Augmentation in VR/AR Applications
Embodiments of the invention provide a camera array imaging architecture that computes depth maps for objects within a scene captured by the cameras, and use a near-field sub-array of cameras to compute depth to near-field objects and a far-field sub-array of cameras to compute depth to far-field objects. In particular, a baseline distance between cameras in the near-field subarray is less than a baseline distance between cameras in the far-field sub-array in order to increase the accuracy of the depth map. Some embodiments provide an illumination near-IR light source for use in computing depth maps.
A method for calibrating a vehicle cabin camera having: a pitch, yaw and roll angle; and a field of view capturing vehicle cabin features which are symmetric about a vehicle longitudinal axis comprises: selecting points from within an image of the vehicle cabin and projecting the points onto a 3D unit circle in accordance with a camera projection model. For each of one or more rotations of a set of candidate yaw and roll rotations, the method comprises: rotating the projected points with the rotation; flipping the rotated points about a pitch axis; counter-rotating the projected points with an inverse of the rotation; and mapping the counter-rotated points back into an image plane to provide a set of transformed points. A candidate rotation which provides a best match between the set of transformed points and the locations of the selected points in the image plane is selected.
A method for calibrating a vehicle cabin camera having: a pitch, yaw and roll angle; and a field of view capturing vehicle cabin features which are symmetric about a vehicle longitudinal axis comprises: selecting points from within an image of the vehicle cabin and projecting the points onto a 3D unit circle in accordance with a camera projection model. For each of one or more rotations of a set of candidate yaw and roll rotations, the method comprises: rotating the projected points with the rotation; flipping the rotated points about a pitch axis; counter-rotating the projected points with an inverse of the rotation; and mapping the counter-rotated points back into an image plane to provide a set of transformed points. A candidate rotation which provides a best match between the set of transformed points and the locations of the selected points in the image plane is selected.
A method of producing an image frame from event packets received from an event camera comprises: forming a tile buffer sized to accumulate event information for a subset of image tiles, the tile buffer having an associated tile table that determines a mapping between each tile of the image frame for which event information is accumulated in the tile buffer and the image frame. For each event packet: an image tile corresponding to the pixel location of the event packet is identified; responsive to the tile buffer storing information for one other event corresponding to the image tile, event information is added to the tile buffer; and responsive to the tile buffer not storing information for another event corresponding to the image tile and responsive to the tile buffer being capable of accumulating event information for at least one more tile, the image tile is added to the tile buffer.
H04N 5/77 - Circuits d'interface entre un appareil d'enregistrement et un autre appareil entre un appareil d'enregistrement et une caméra de télévision
B60R 11/04 - Montage des caméras pour fonctionner pendant la marche; Disposition de leur commande par rapport au véhicule
H04N 5/14 - Circuits de signal d'image pour le domaine des fréquences vidéo
H04N 5/91 - Traitement du signal de télévision pour l'enregistrement
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c. à d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04N 25/00 - Circuits de capteurs d'images à l'état solide [capteurs SSIS]; Leur commande
H04N 25/772 - Circuits de pixels, p.ex. mémoires, convertisseurs A/N, amplificateurs de pixels, circuits communs ou composants communs comprenant des convertisseurs A/N, V/T, V/F, I/T ou I/F
B60R 11/00 - Autres aménagements pour tenir ou monter des objets
A method comprises displaying a first image acquired from a camera having an input camera projection model including a first focal length and an optical axis parameter value. A portion of the first image is selected as a second image associated with an output camera projection model in which either a focal length and/or an optical axis parameter value differ from the parameters of the input camera projection model. The method involves iteratively: adjusting either the focal length and/or an optical axis parameter value for the camera lens so that it approaches the corresponding value of the output camera projection model; acquiring a subsequent image using the adjusted focal length or optical axis parameter value; mapping pixel coordinates in the second image, through a normalized 3D coordinate system to respective locations in the subsequent image to determine respective values for the pixel coordinates; and displaying the second image.
A method comprises displaying a first image acquired from a camera having an input camera projection model including a first focal length and an optical axis parameter value. A portion of the first image is selected as a second image associated with an output camera projection model in which either a focal length and/or an optical axis parameter value differ from the parameters of the input camera projection model. The method involves iteratively: adjusting either the focal length and/or an optical axis parameter value for the camera lens so that it approaches the corresponding value of the output camera projection model; acquiring a subsequent image using the adjusted focal length or optical axis parameter value; mapping pixel coordinates in the second image, through a normalized 3D coordinate system to respective locations in the subsequent image to determine respective values for the pixel coordinates; and displaying the second image.
A method for correcting an image divides an output image into a grid with vertical sections of width smaller than the image width but wide enough to allow efficient bursts when writing distortion corrected line sections into memory. A distortion correction engine includes a relatively small amount of memory for an input image buffer but without requiring unduly complex control. The input image buffer accommodates enough lines of an input image to cover the distortion of a single most vertically distorted line section of the input image. The memory required for the input image buffer can be significantly less than would be required to store all the lines of a distorted input image spanning a maximal distortion of a complete line within the input image.
A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
G06T 7/269 - Analyse du mouvement utilisant des procédés basé sur le gradient
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
A method for training a neural network for detecting a plurality of classes of object within a sample comprises providing a training data set comprising a plurality of samples, each annotated according to whether the samples include labelled objects of interest. In a first type of samples, all objects of interest are labelled according to their class and comprise a foreground of said samples, the remainder of the samples comprising background. In a second type of samples, some objects of interest are labelled in a foreground and their background may comprise unlabelled objects. A third type of samples comprise only background comprising no objects of interest. Negative mining is only performed on the results of processing the first and third types of samples.
Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.
G06V 40/18 - Caractéristiques de l’œil, p.ex. de l’iris
G06T 7/50 - Récupération de la profondeur ou de la forme
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
A method for producing a textural image from event information generated by an event camera comprises: accumulating event information from a plurality of events occurring during successive event cycles across a field of view of the event camera, each event indicating an x,y location within the field of view, a polarity for a change of detected light intensity incident at the x,y location and an event cycle at which the event occurred; in response to selected event cycles, analysing event information for one or more preceding event cycles to identify one or more regions of interest bounding a respective object to be tracked; and responsive to a threshold event criterion for a region of interest being met, generating a textural image for the region of interest from event information accumulated from within the region of interest.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 20/52 - Activités de surveillance ou de suivi, p.ex. pour la reconnaissance d’objets suspects
Disclosed is a multi-modal convolutional neural network (CNN) for fusing image information from a frame based camera, such as, a near infra-red (NIR) camera and an event camera for analysing facial characteristics in order to produce classifications such as head pose or eye gaze. The neural network processes image frames acquired from each camera through a plurality of convolutional layers to provide a respective set of one or more intermediate images. The network fuses at least one corresponding pair of intermediate images generated from each of image frames through an array of fusing cells. Each fusing cell is connected to at least a respective element of each intermediate image and is trained to weight each element from each intermediate image to provide the fused output. The neural network further comprises at least one task network configured to generate one or more task outputs for the region of interest.
Disclosed is a multi-modal convolutional neural network (CNN) for fusing image information from a frame based camera, such as, a near infra-red (NIR) camera and an event camera for analysing facial characteristics in order to produce classifications such as head pose or eye gaze. The neural network processes image frames acquired from each camera through a plurality of convolutional layers to provide a respective set of one or more intermediate images. The network fuses at least one corresponding pair of intermediate images generated from each of image frames through an array of fusing cells. Each fusing cell is connected to at least a respective element of each intermediate image and is trained to weight each element from each intermediate image to provide the fused output. The neural network further comprises at least one task network configured to generate one or more task outputs for the region of interest.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06V 10/80 - Fusion, c. à d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
A neural network image processing apparatus arranged to acquire images from an image sensor and to: identify a ROI containing a face region in an image; determine at plurality of facial landmarks in the face region; use the facial landmarks to transform the face region within the ROI into a face region having a given pose; and use transformed landmarks within the transformed face region to identify a pair of eye regions within the transformed face region. Each identified eye region is fed to a respective first and second convolutional neural network, each network configured to produce a respective feature vector. Each feature vector is fed to respective eyelid opening level neural networks to obtain respective measures of eyelid opening for each eye region. The feature vectors are combined and to a gaze angle neural network to generate gaze yaw and pitch values substantially simultaneously with the eyelid opening values.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
A video super resolution method comprises successively executing instances of a first plurality of layers (SISR) of a neural network for generating a first image (St) at a higher resolution than an input image frame (Xt); successively executing a second plurality of layers (VSR) of the neural network for generating a second image (Vt) at the higher resolution, at least one of the second plurality of layers generating intermediate output information (Ht), the second plurality of layers taking into account an output image (Yt−1) at the higher resolution generated by a previous instance of the network from a previous input image frame (Xt−1) and intermediate output information (Ht−1) generated by the second plurality of layers of the previous instance, and executing a third plurality of layers for combining the first (St) and second (Vt) images to produce an output image (Yt) for the instance of the network.
G06T 5/10 - Amélioration ou restauration d'image en utilisant le filtrage dans le domaine non spatial
G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
H04N 23/951 - Systèmes de photographie numérique, p. ex. systèmes d'imagerie par champ lumineux en utilisant plusieurs images pour influencer la résolution, la fréquence d'images ou le rapport de cadre
The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
A method of producing an image frame from event packets received from an event camera comprises: forming a tile buffer sized to accumulate event information for a subset of image tiles, the tile buffer having an associated tile table that determines a mapping between each tile of the image frame for which event information is accumulated in the tile buffer and the image frame. For each event packet: an image tile corresponding to the pixel location of the event packet is identified; responsive to the tile buffer storing information for one other event corresponding to the image tile, event information is added to the tile buffer; and responsive to the tile buffer not storing information for another event corresponding to the image tile and responsive to the tile buffer being capable of accumulating event information for at least one more tile, the image tile is added to the tile buffer.
H04N 5/335 - Transformation d'informations lumineuses ou analogues en informations électriques utilisant des capteurs d'images à l'état solide [capteurs SSIS]
A method of producing an image frame from event packets received from an event camera comprises: forming a tile buffer sized to accumulate event information for a subset of image tiles, the tile buffer having an associated tile table that determines a mapping between each tile of the image frame for which event information is accumulated in the tile buffer and the image frame. For each event packet: an image tile corresponding to the pixel location of the event packet is identified; responsive to the tile buffer storing information for one other event corresponding to the image tile, event information is added to the tile buffer; and responsive to the tile buffer not storing information for another event corresponding to the image tile and responsive to the tile buffer being capable of accumulating event information for at least one more tile, the image tile is added to the tile buffer.
H04N 5/77 - Circuits d'interface entre un appareil d'enregistrement et un autre appareil entre un appareil d'enregistrement et une caméra de télévision
B60R 11/04 - Montage des caméras pour fonctionner pendant la marche; Disposition de leur commande par rapport au véhicule
H04N 5/14 - Circuits de signal d'image pour le domaine des fréquences vidéo
H04N 5/91 - Traitement du signal de télévision pour l'enregistrement
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c. à d. systèmes dans lesquels le signal vidéo n'est pas diffusé
B60R 11/00 - Autres aménagements pour tenir ou monter des objets
39.
MEASUREMENT OF AN IMAGE SENSOR POINT SPREAD FUNCTION (PSF)
Techniques and arrangements that utilize speckle imaging and autocorrelation to estimate the PSF of an image sensor for a digital imaging apparatus, e.g., a camera or a scanner. In particular, a system of components described herein is a simple arrangement that does not require a complex setup. Therefore, the system is portable and easy to set up. Additionally, by utilizing autocorrelation, the calculations of PSF using data obtained by the system are simplified.
A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.
G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
G06V 20/30 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p.ex. des photos ou des vidéos issus des réseaux sociaux
G06F 18/22 - Critères d'appariement, p.ex. mesures de proximité
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
G06F 18/2113 - Sélection du sous-ensemble de caractéristiques le plus significatif en classant ou en filtrant l'ensemble des caractéristiques, p.ex. en utilisant une mesure de la variance ou de la corrélation croisée des caractéristiques
41.
METHODS AND SYSTEMS TO PREDICT ACTIVITY IN A SEQUENCE OF IMAGES
A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
42.
Method and system to determine the location and/or orientation of a head
A method for determining an absolute depth map to monitor the location and pose of a head (100) being imaged by a camera comprises: acquiring (20) an image from the camera (110) including a head with a facial region; determining (23) at least one distance from the camera (110) to a facial feature of the facial region using a distance measuring sub-system (120); determining (24) a relative depth map of facial features within the facial region; and combining (25) the relative depth map with the at least one distance to form an absolute depth map for the facial region.
A method for producing a textural image from event information generated by an event camera comprises: accumulating event information from a plurality of events occurring during successive event cycles across a field of view of the event camera, each event indicating an x,y location within the field of view, a polarity for a change of detected light intensity incident at the x,y location and an event cycle at which the event occurred; in response to selected event cycles, analysing event information for one or more preceding event cycles to identify one or more regions of interest bounding a respective object to be tracked; and responsive to a threshold event criterion for a region of interest being met, generating a textural image for the region of interest from event information accumulated from within the region of interest.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
44.
Event detector and method of generating textural image based on event count decay factor and net polarity
A method for producing a textural image from event information generated by an event camera comprises: accumulating event information from a plurality of events occurring during successive event cycles across a field of view of the event camera, each event indicating an x,y location within the field of view, a polarity for a change of detected light intensity incident at the x,y location and an event cycle at which the event occurred; in response to selected event cycles, analysing event information for one or more preceding event cycles to identify one or more regions of interest bounding a respective object to be tracked; and responsive to a threshold event criterion for a region of interest being met, generating a textural image for the region of interest from event information accumulated from within the region of interest.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
A method for determining an absolute depth map to monitor the location and pose of a head (100) being imaged by a camera comprises: acquiring (20) an image from the camera (110) including a head with a facial region; determining (23) at least one distance from the camera (110) to a facial feature of the facial region using a distance measuring sub-system (120); determining (24) a relative depth map of facial features within the facial region; and combining (25) the relative depth map with the at least one distance to form an absolute depth map for the facial region.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
An image processing system is configured to receive a first high resolution stream of images and a second lower resolution stream of images from image sources with substantially the same field of view. The system comprises a localizer component configured to provide a location for any object of interest independently of class within successive images of the second stream of images; a classifier configured to: receive one or more locations selectively provided by the localizer, identify a corresponding portion of an image acquired from the first stream at substantially the same time at which an image from the second stream in which an object of interest was identified and return a classification for the type of object within the identified portion of the image from the first stream; and a tracker configured to associate the classification with the location through acquisition of successive images in the second stream.
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c. à d. systèmes dans lesquels le signal vidéo n'est pas diffusé
G06V 20/52 - Activités de surveillance ou de suivi, p.ex. pour la reconnaissance d’objets suspects
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
A method for producing a textural image from event information generated by an event camera comprises: accumulating event information from a plurality of events occurring during successive event cycles across a field of view of the event camera, each event indicating an x,y location within the field of view, a polarity for a change of detected light intensity incident at the x,y location and an event cycle at which the event occurred; in response to selected event cycles, analysing event information for one or more preceding event cycles to identify one or more regions of interest bounding a respective object to be tracked; and responsive to a threshold event criterion for a region of interest being met, generating a textural image for the region of interest from event information accumulated from within the region of interest.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
Systems and methods of detecting an unauthorized data insertion into a stream of data segments extending between electronic modules or between electronic components within a module, wherein a Secret embedded into the data stream is compared to a Replica Secret upon receipt to confirm data transmission integrity.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06T 1/00 - Traitement de données d'image, d'application générale
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
G06V 40/18 - Caractéristiques de l’œil, p.ex. de l’iris
A method operable by a computing device for configuring access for a limited user interface (UI) device to a network service via a local network access point is disclosed. The method comprises the steps of: obtaining from the limited UI device a device identifier via a first out-of-band channel. The device identifier is provided to the network service via a secure network link. A zero knowledge proof (ZKP) challenge is received from the network service. Configuration information is provided to the limited-UI device via a second out-of-band channel, the configuration information including information sufficient to enable the limited-UI device to connect to the local network access point. The ZKP challenge is provided to the limited-UI device via the second out-of-band channel. A secure channel key is received from the network service indicating a successful response from the limited-UI device to the ZKP challenge; and provided to the limited-UI device enabling the limited-UI device to access the network service.
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
H04W 76/10 - Gestion de la connexion Établissement de la connexion
A method for stabilizing a video sequence comprises: obtaining an indication of camera movement from acquisition of a previous camera frame to acquisition of a current camera frame; determining an orientation for the camera at a time of acquiring the current camera frame; and determining a candidate orientation for a crop frame for the current camera frame by adjusting an orientation of a crop frame associated with the previous camera frame according to the determined orientation. A boundary of one of the camera frame or crop frame is traversed to determine if a specific point on the boundary of the crop frame exceeds a boundary of the camera frame. If so, a rotation of the specific point location which would bring the specific point location onto the boundary of the crop frame is determined and the candidate crop frame orientation updated accordingly before the crop frame is displayed.
Systems and methods for calibrating an array camera are disclosed. Systems and methods for calibrating an array camera in accordance with embodiments of this invention include the capturing of an image of a test pattern with the array camera such that each imaging component in the array camera captures an image of the test pattern. The image of the test pattern captured by a reference imaging component is then used to derive calibration information for the reference component. A corrected image of the test pattern for the reference component is then generated from the calibration information and the image of the test pattern captured by the reference imaging component. The corrected image is then used with the images captured by each of the associate imaging components associated with the reference component to generate calibration information for the associate imaging components.
H04N 13/282 - Générateurs de signaux d’images pour la génération de signaux d’images correspondant à au moins trois points de vue géométriques, p.ex. systèmes multi-vues
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c. à d. étalonnage de caméra
H04N 17/02 - Diagnostic, test ou mesure, ou leurs détails, pour les systèmes de télévision pour les signaux de télévision en couleurs
A video super resolution method comprises successively executing instances of a first plurality of layers (SISR) of a neural network for generating a first image (St) at a higher resolution than an input image frame (Xt); successively executing a second plurality of layers (VSR) of the neural network for generating a second image (Vt) at the higher resolution, at least one of the second plurality of layers generating intermediate output information (Ht), the second plurality of layers taking into account an output image (Yt−1) at the higher resolution generated by a previous instance of the network from a previous input image frame (Xt−1) and intermediate output information (Ht−1) generated by the second plurality of layers of the previous instance, and executing a third plurality of layers for combining the first (St) and second (Vt) images to produce an output image (Yt) for the instance of the network.
A device, such as a head-mounted device (HMD), may include a frame and a plurality of mirrors coupled to an interior portion of the frame. An imaging device may be coupled to the frame at a position to capture images of an eye of the wearer reflected from the mirrors. The HMD may also include a mirror angle adjustment device to adjust an angle of one or more of the mirrors relative to the imaging device so that the mirror(s) reflect the eye of the wearer to the imaging device.
G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,
G02B 27/09 - Mise en forme du faisceau, p.ex. changement de la section transversale, non prévue ailleurs
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A hardware acceleration module may generate a channel-wise argmax map using a predefined set of hardware-implemented operations. In some examples, a hardware acceleration module may receive a set of feature maps for different image channels. The hardware acceleration module may execute a sequence of hardware operations, including a portion(s) of hardware for executing a convolution, rectified linear unit (ReLU) activation, and/or layer concatenation, to determine a maximum channel feature value and/or argument maxima (argmax) value for a set of associated locations within the feature maps. An argmax map may be generated based at least in part on the argument maximum for a set of associated locations.
G06F 30/331 - Vérification de la conception, p.ex. simulation fonctionnelle ou vérification du modèle par simulation avec accélération matérielle, p.ex. en utilisant les réseaux de portes programmables [FPGA] ou une émulation
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images utilisant des procédés basés sur les caractéristiques
G06F 7/483 - Calculs avec des nombres représentés par une combinaison non linéaire de nombres codés, p.ex. nombres rationnels, système de numération logarithmique ou nombres à virgule flottante
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A method operable within an image capture device for stabilizing a sequence of images captured by the image capture device is disclosed. The method comprises, using lens based sensors indicating image capture device movement during image acquisition, performing optical image stabilization (OIS) during acquisition of each image of the sequence of images to provide a sequence of OIS corrected images. Movement of the device for each frame during which each OIS corrected image is captured is determined using inertial measurement sensors. At least an estimate of OIS control performed during acquisition of an image is obtained. The estimate is removed from the intra-frame movement determined for the frame during which the OIS corrected image was captured to provide a residual measurement of movement for the frame. Electronic image stabilization (EIS) of each OIS corrected image based on the residual measurement is performed to provide a stabilized sequence of images.
A method includes determining, by a portable device, image capturing conditions based on an analysis of contents of a first digital image of a group of digital images captured by an image capturing device, and determining, by the portable device, whether the image capturing conditions determined for the first digital image indicate outdoor image capturing conditions. Based at least in part on a determination that the image capturing conditions determined for the first digital image indicate outdoor image capturing conditions, displaying a first indication that the first digital image must be captured in indoor image capturing conditions for an iris code enrollment process, and displaying a second indication of resumption of the iris code enrollment process when the image capturing conditions, determined for the first digital image, indicate the indoor image capturing conditions.
An image acquisition system determines first and second sets of points defining an iris-pupil boundary and an iris-sclera boundary in an acquired image; determines respective ellipses fitting the first and second sets of points; determines a transformation to transform one of the ellipses into a circle on a corresponding plane; using the determined transformation, transforms the selected ellipse into a circle on the plane; using the determined transformation, transforms the other ellipse into a transformed ellipse on the plane; determines a plurality of ellipses on the plane for defining an iris grid, by interpolating a plurality of ellipses between the circle and the transformed ellipse; moves the determined grid ellipses onto the iris in the image using an inverse transformation of the determined transformation; and extracts an iris texture by unwrapping the iris and interpolating image pixel values at each grid point defined along each of the grid ellipses.
A camera comprises a lens assembly coupled to an event-sensor, the lens assembly being configured to focus a light field onto a surface of the event-sensor, the event-sensor surface comprising a plurality of light sensitive-pixels, each of which cause an event to be generated when there is a change in light intensity greater than a threshold amount incident on the pixel. The camera further includes an actuator which can be triggered to cause a change in the light field incident on the surface of the event-sensor and to generate a set of events from a sub-set of pixels distributed across the surface of the event-sensor.
G06K 9/60 - Combinaison de l'obtention de l'image et des fonctions de prétraitement
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
H04N 5/341 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner
59.
Vehicle side view camera system with adjustable field of view
A vehicle can include one or more movably mounted cameras that are able to move to adjust a viewing angle of the camera(s) relative to a body of the vehicle. The camera(s) may be movable by virtue of being coupled to a movable side mirror, such that movement of the side mirror changes a field of view of the camera coupled to the side mirror. For example, the side mirror can be rotated about a rotational axis between a first position in which a field of view of the camera is directed in a first direction (e.g. toward a ground proximate the vehicle), and a second position in which the field of view of the camera is directed in a second direction, different than the first direction (e.g., outward from the vehicle or toward a door of the vehicle).
B60R 25/30 - Détection relative au vol ou autres événements relatifs aux systèmes antivol
B60R 1/12 - Ensembles de miroirs combinés avec d'autres objets, p.ex. pendules
B60R 25/20 - Moyens pour enclencher ou arrêter le système antivol
B60R 25/102 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme un signal étant envoyé vers un lieu distant, p.ex. signal radio transmis à un poste de police, à une entreprise de sécurité ou au propriétaire du véhicule
B60R 25/104 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme caractérisé par le type de signal antivol, p.ex. signaux visuels ou audibles ayant des caractéristiques spéciales
B60R 25/10 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme
G06T 7/136 - Découpage; Détection de bords impliquant un seuillage
B60R 1/07 - Dispositions pour les rétroviseurs montés à l'extérieur du véhicule avec une commande à distance pour régler leur position avec un organe actionné par l'énergie électrique
A handheld computing device comprises a display comprising an array of pixels illuminated by a plurality of visible light sources, and a plurality of infra-red light sources interleaved between the visible light sources, the IR light sources being actuable to emit diffuse IR light with a first intensity. A camera has an image sensor comprising an array of pixels responsive to infra-red light and a lens assembly with an optical axis extending from the image sensor through the surface of the display. A dedicated illumination source is located outside the display and is actuable to emit infra-red light with a second greater intensity. A processor is configured to switch between an iris region processing mode in which a subject is illuminated at least by the dedicated light source and a face region processing mode in which a subject is illuminated by the plurality of IR light sources.
A camera comprises a lens assembly coupled to an event-sensor, the lens assembly being configured to focus a light field onto a surface of the event-sensor, the event-sensor surface comprising a plurality of light sensitive-pixels, each of which cause an event to be generated when there is a change in light intensity greater than a threshold amount incident on the pixel. The camera further includes an actuator which can be triggered to cause a change in the light field incident on the surface of the event-sensor and to generate a set of events from a sub-set of pixels distributed across the surface of the event-sensor.
H04N 5/345 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner en lisant partiellement une matrice de capteurs SSIS
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G02B 27/64 - Systèmes pour donner des images utilisant des éléments optiques pour la stabilisation latérale et angulaire de l'image
Systems and methods in accordance with embodiments of the invention are disclosed that use super-resolution (SR) processes to use information from a plurality of low resolution (LR) images captured by an array camera to produce a synthesized higher resolution image. One embodiment includes obtaining input images using the plurality of imagers, using a microprocessor to determine an initial estimate of at least a portion of a high resolution image using a plurality of pixels from the input images, and using a microprocessor to determine a high resolution image that when mapped through the forward imaging transformation matches the input images to within at least one predetermined criterion using the initial estimate of at least a portion of the high resolution image. In addition, each forward imaging transformation corresponds to the manner in which each imager in the imaging array generate the input images, and the high resolution image synthesized by the microprocessor has a resolution that is greater than any of the input images.
A method for stabilizing a video sequence comprises: obtaining an indication of camera movement from acquisition of a previous camera frame to acquisition of a current camera frame; determining an orientation for the camera at a time of acquiring the current camera frame; and determining a candidate orientation for a crop frame for the current camera frame by adjusting an orientation of a crop frame associated with the previous camera frame according to the determined orientation. A boundary of one of the camera frame or crop frame is traversed to determine if a specific point on the boundary of the crop frame exceeds a boundary of the camera frame. If so, a rotation of the specific point location which would bring the specific point location onto the boundary of the crop frame is determined and the candidate crop frame orientation updated accordingly before the crop frame is displayed.
The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.
Systems and methods for estimating depth from projected texture using camera arrays are described. A camera array includes a conventional camera and at least one two-dimensional array of cameras, where the conventional camera has a higher resolution than the cameras in the at least one two-dimensional array of cameras, an illumination system configured to illuminate a scene with a projected texture, where an image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture, capture a set of images of the scene illuminated with the projected texture, and determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images.
G01B 11/22 - Dispositions pour la mesure caractérisées par l'utilisation de techniques optiques pour mesurer la profondeur
G01B 11/25 - Dispositions pour la mesure caractérisées par l'utilisation de techniques optiques pour mesurer des contours ou des courbes en projetant un motif, p.ex. des franges de moiré, sur l'objet
G06T 7/521 - Récupération de la profondeur ou de la forme à partir de la projection de lumière structurée
G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
G06T 7/557 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir des champs de lumière, p.ex. de caméras plénoptiques
A method and system for detecting facial expressions in digital images and applications therefore are disclosed. Analysis of a digital image determines whether or not a smile and/or blink is present on a person's face. Face recognition, and/or a pose or illumination condition determination, permits application of a specific, relatively small classifier cascade.
A method of generating landmark locations for an image crop comprises: processing the crop through an encoder-decoder to provide a plurality of N output maps of comparable spatial resolution to the crop, each output map corresponding to a respective landmark of an object appearing in the image crop; processing an output map from the encoder through a plurality of feed forward layers to provide a feature vector comprising N elements, each element including an (x,y) location for a respective landmark. Any landmarks locations from the feature vector having an x or a y location outside a range for a respective row or column of the crop are selected for a final set of landmark locations; with remaining landmark locations tending to be selected from the N (x,y) landmark locations from the plurality of N output maps.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
Embodiments of the invention provide a camera array imaging architecture that computes depth maps for objects within a scene captured by the cameras, and use a near-field sub-array of cameras to compute depth to near-field objects and a far-field sub-array of cameras to compute depth to far-field objects. In particular, a baseline distance between cameras in the near-field subarray is less than a baseline distance between cameras in the far-field sub-array in order to increase the accuracy of the depth map. Some embodiments provide an illumination near-IR light source for use in computing depth maps.
A method for correcting an image divides an output image into a grid with vertical sections of width smaller than the image width but wide enough to allow efficient bursts when writing distortion corrected line sections into memory. A distortion correction engine includes a relatively small amount of memory for an input image buffer but without requiring unduly complex control. The input image buffer accommodates enough lines of an input image to cover the distortion of a single most vertically distorted line section of the input image. The memory required for the input image buffer can be significantly less than would be required to store all the lines of a distorted input image spanning a maximal distortion of a complete line within the input image.
A method for automatically determining exposure settings for an image acquisition system comprises maintaining a plurality of look-up tables, each look-up table being associated with a corresponding light condition and storing image exposure settings associated with corresponding distance values between a subject and the image acquisition system. An image of a subject is acquired from a camera module; and a light condition occurring during the acquisition is determined based on the acquired image. A distance between the subject and the camera module during the acquisition is calculated. The method then determines whether a correction of the image exposure settings for the camera module is required based on the calculated distance and the determined light condition; and responsive to correction being required: selects image exposure settings corresponding to the calculated distance from the look-up table corresponding to the determined light condition; and acquires a new image using the selected image exposure settings.
G06V 40/00 - Reconnaissance de formes biométriques, liées aux êtres humains ou aux animaux, dans les données d’image ou vidéo
H04N 5/235 - Circuits pour la compensation des variations de la luminance de l'objet
G01S 3/00 - Radiogoniomètres pour déterminer la direction d'où proviennent des ondes infrasonores, sonores, ultrasonores ou électromagnétiques ou des émissions de particules sans caractéristiques de direction
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
H04N 5/243 - Circuits pour la compensation des variations de la luminance de l'objet en agissant sur le signal d'image
G08B 13/196 - Déclenchement influencé par la chaleur, la lumière, ou les radiations de longueur d'onde plus courte; Déclenchement par introduction de sources de chaleur, de lumière, ou de radiations de longueur d'onde plus courte utilisant des systèmes détecteurs de radiations passifs utilisant des systèmes de balayage et de comparaison d'image utilisant des caméras de télévision
71.
Capturing and processing of images including occlusions focused on an image sensor by a lens stack array
Systems and methods for implementing array cameras configured to perform super-resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. An imaging device in accordance with one embodiment of the invention includes at least one imager array, and each imager in the array comprises a plurality of light sensing elements and a lens stack including at least one lens surface, where the lens stack is configured to form an image on the light sensing elements, control circuitry configured to capture images formed on the light sensing elements of each of the imagers, and a super-resolution processing module configured to generate at least one higher resolution super-resolved image using a plurality of the captured images.
H04N 5/365 - Traitement du bruit, p.ex. détection, correction, réduction ou élimination du bruit appliqué au bruit à motif fixe, p.ex. non-uniformité de la réponse
H04N 13/128 - Ajustement de la profondeur ou de la disparité
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
H04N 5/33 - Transformation des rayonnements infrarouges
H04N 5/341 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner
H04N 5/349 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner pour accroître la résolution en déplaçant le capteur par rapport à la scène
H04N 5/357 - Traitement du bruit, p.ex. détection, correction, réduction ou élimination du bruit
G06T 7/557 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir des champs de lumière, p.ex. de caméras plénoptiques
H04N 13/239 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques utilisant deux capteurs d’images 2D dont la position relative est égale ou en correspondance à l’intervalle oculaire
G06T 7/50 - Récupération de la profondeur ou de la forme
H04N 9/097 - Dispositions optiques associées aux dispositifs analyseurs, p.ex. pour partager des faisceaux, pour corriger la couleur
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
H04N 9/09 - Générateurs de signaux d'image avec plusieurs têtes de lecture
H04N 9/73 - Circuits pour l'équilibrage des couleurs, p.ex. circuits pour équilibrer le blanc ou commande de la température de couleur
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G01C 25/00 - Fabrication, étalonnage, nettoyage ou réparation des instruments ou des dispositifs mentionnés dans les autres groupes de la présente sous-classe
This disclosure describes, in part, devices and techniques for performing biometric identification for an electronic device. For instance, the electronic device may include one or more near-infrared illuminators that output near-infrared light. The one or more near-infrared illuminators may be located in or on a bezel and/or a display of the electronic device. The electronic device may also include an imaging device that generates first image data representing the near-infrared light and visible light. After generating the image data, the electronic device may process the first image data using one or more image processing techniques to generate second image data representing a near-infrared image and third image data representing a visible image. The electronic device may then analyze the second image data and/or the third image data using one or more biometric identification techniques. Based on the analysis, the electronic device may identify a person possessing the electronic device.
An image processing system comprises a template matching engine (TME). The TME reads an image from the memory; and as each pixel of the image is being read, calculates a respective feature value of a plurality of feature maps as a function of the pixel value. A pre-filter is responsive to a current pixel location comprising a node within a limited detector cascade to be applied to a window within the image to: compare a feature value from a selected one of the plurality of feature maps corresponding to the pixel location to a threshold value; and responsive to pixels for all nodes within a limited detector cascade to be applied to the window having been read, determine a score for the window. A classifier, responsive to the pre-filter indicating that a score for a window is below a window threshold, does not apply a longer detector cascade to the window before indicating that the window does not comprise an object to be detected.
G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
75.
Multi-camera vision system and method of monitoring
A multi-camera vision system and method of monitoring. In one embodiment imaging systems provide object classifications with cameras positioned to receive image data from a field of view to classify an object among multiple classifications. A control unit receives classification or position information of objects and (ii) displays an image corresponding to a classified object relative to the position of the structure. An embodiment of a related method monitors positions of an imaged object about a boundary by continually capturing at least first and second series of image frames, each series comprising different fields of view of a scene about the boundary, with some of the image frames in the first series covering a wide angle field of view and some of the image frames in the second series covering no more than a narrow angle field of view.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c. à d. systèmes dans lesquels le signal vidéo n'est pas diffusé
An image acquisition system determines first and second sets of points defining an iris-pupil boundary and an iris-sclera boundary in an acquired image; determines respective ellipses fitting the first and second sets of points; determines a transformation to transform one of the ellipses into a circle on a corresponding plane; using the determined transformation, transforms the selected ellipse into a circle on the plane; using the determined transformation, transforms the other ellipse into a transformed ellipse on the plane; determines a plurality of ellipses on the plane for defining an iris grid, by interpolating a plurality of ellipses between the circle and the transformed ellipse; moves the determined grid ellipses onto the iris in the image using an inverse transformation of the determined transformation; and extracts an iris texture by unwrapping the iris and interpolating image pixel values at each grid point defined along each of the grid ellipses.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
A neural network image processing apparatus arranged to acquire images from an image sensor and to: identify a ROI containing a face region in an image; determine at plurality of facial landmarks in the face region; use the facial landmarks to transform the face region within the ROI into a face region having a given pose; and use transformed landmarks within the transformed face region to identify a pair of eye regions within the transformed face region. Each identified eye region is fed to a respective first and second convolutional neural network, each network configured to produce a respective feature vector. Each feature vector is fed to respective eyelid opening level neural networks to obtain respective measures of eyelid opening for each eye region. The feature vectors are combined and to a gaze angle neural network to generate gaze yaw and pitch values substantially simultaneously with the eyelid opening values.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
In an embodiment, a 3D facial modeling system includes a plurality of cameras configured to capture images from different viewpoints, a processor, and a memory containing a 3D facial modeling application and parameters defining a face detector, wherein the 3D facial modeling application directs the processor to obtain a plurality of images of a face captured from different viewpoints using the plurality of cameras, locate a face within each of the plurality of images using the face detector, wherein the face detector labels key feature points on the located face within each of the plurality of images, determine disparity between corresponding key feature points of located faces within the plurality of images, and generate a 3D model of the face using the depth of the key feature points.
G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation
G06T 7/149 - Découpage; Détection de bords impliquant des modèles déformables, p.ex. des modèles de contours actifs
G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
79.
Facial features tracker with advanced training for natural rendering of human faces in real-time
Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.
This disclosure describes, in part, systems and techniques for performing eye tracking. For instance, a system may include a first imaging device that generates first image data. The system may then analyze the first image data to determine a location of a face of a user. Using the location, the system may cause an actuator to move from a first position to a second position in order to direct a second imaging device towards the face of the user. While in the second position, the second imaging device may generate second image data representing at least the face of the user. The system may then analyze the second image data to determine a gaze direction of the user. In some instances, the first imaging device may include a first field of view (FOV) that is greater than a second FOV of the second imaging device.
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
B60Q 3/18 - Circuits; Agencements de commande pour faire varier l’intensité de la lumière
B60R 11/04 - Montage des caméras pour fonctionner pendant la marche; Disposition de leur commande par rapport au véhicule
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
B60R 11/00 - Autres aménagements pour tenir ou monter des objets
B60W 40/08 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conducteurs ou aux passagers
81.
System for performing eye detection and/or tracking
This disclosure describes, in part, systems and techniques for performing eye tracking. For instance, a system may include a first imaging device that generates first image data. The system may then analyze the first image data to determine a location of a face of a user. Using the location, the system may cause an actuator to move from a first position to a second position in order to direct a second imaging device towards the face of the user. While in the second position, the second imaging device may generate second image data representing at least the face of the user. The system may then analyze the second image data to determine a gaze direction of the user. In some instances, the first imaging device may include a first field of view (FOV) that is greater than a second FOV of the second imaging device.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
H04N 5/247 - Disposition des caméras de télévision
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
82.
Method of providing a sharpness measure for an image
A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.
G06T 7/42 - Analyse de la texture basée sur la description statistique de texture utilisant des procédés de transformation de domaine
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A biometrics-enabled portable storage device may store and secure data via biometrics related to a user's iris. The biometrics-enabled portable storage device may include a camera that captures image data related a user's iris and stores the image data to enroll the user for use of the biometrics-enabled portable storage device. To unlock the data, a user aligns the camera with their iris using a hot mirror and the camera captures iris data for comparison with the iris image data stored during enrollment. If the two sets of image data match, the biometrics-enabled portable storage device may be unlocked and the user may access data stored on the biometrics-enabled portable storage device. If the two sets of image data do not match, then the biometrics-enabled portable storage device remains locked.
A neural network engine comprises a plurality of floating point multipliers, each having an input connected to an input map value and an input connected to a corresponding kernel value. Pairs of multipliers provide outputs to a tree of nodes, each node of the tree being configured to provide a floating point output corresponding to either: a larger of the inputs of the node; or a sum of the inputs, one output node of the tree providing a first input of an output module, and one of the multipliers providing an output to a second input of the output module. The engine is configured to process either a convolution layer of a neural network, an average pooling layer or a max pooling layer according to the kernel values and whether the nodes and output module are configured to output a larger or a sum of their inputs.
Systems and methods for calibrating an array camera are disclosed. Systems and methods for calibrating an array camera in accordance with embodiments of this invention include the capturing of an image of a test pattern with the array camera such that each imaging component in the array camera captures an image of the test pattern. The image of the test pattern captured by a reference imaging component is then used to derive calibration information for the reference component. A corrected image of the test pattern for the reference component is then generated from the calibration information and the image of the test pattern captured by the reference imaging component. The corrected image is then used with the images captured by each of the associate imaging components associated with the reference component to generate calibration information for the associate imaging components.
H04N 13/282 - Générateurs de signaux d’images pour la génération de signaux d’images correspondant à au moins trois points de vue géométriques, p.ex. systèmes multi-vues
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c. à d. étalonnage de caméra
H04N 17/02 - Diagnostic, test ou mesure, ou leurs détails, pour les systèmes de télévision pour les signaux de télévision en couleurs
86.
Method of generating a digital video image using a wide-angle field of view lens
A method of generating a digital video image uses a wide-angle field of view (WFOV) lens positioned closely in front of an image sensor array so that the image field of the lens is so curved at the sensor array that different regions of the image field are substantially in focus on the sensor array for different positions of the lens. The method comprises selecting a desired region of interest in the image field of the lens, and adjusting the lens/array distance to bring the region of interest into focus on the sensor array. The in-focus region of interest is stored and at least partially corrected for field-of-view distortion due to the WFOV lens. The corrected image is displayed, locally and/or remotely. These steps are cyclically repeated to provide the video image.
H04N 23/69 - Commande de moyens permettant de modifier l'angle du champ de vision, p. ex. des objectifs de zoom optique ou un zoom électronique
H04N 23/61 - Commande des caméras ou des modules de caméras en fonction des objets reconnus
H04N 25/61 - Traitement du bruit, p.ex. détection, correction, réduction ou élimination du bruit le bruit provenant uniquement de l'objectif, p. ex. l'éblouissement, l'ombrage, le vignettage ou le "cos4"
H04N 23/63 - Commande des caméras ou des modules de caméras en utilisant des viseurs électroniques
A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
G06T 7/269 - Analyse du mouvement utilisant des procédés basé sur le gradient
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
A multi-processor neural network processing apparatus comprises: a plurality of network processing engines, each for processing one or more layers of a neural network according to a network configuration. A memory at least temporarily stores network configuration information, input image information, intermediate image information and output information for the network processing engines. At least one of the network processing engines is configured, when otherwise idle, to identify configuration information and input image information to be processed by another target network processing engine and to use the configuration information and input image information to replicate the processing of the target network processing engine. The apparatus is configured to compare at least one portion of information output by the target network processing engine with corresponding information generated by the network processing engine to determine if either the target network processing engine or the network processing engine is operating correctly.
G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A depth sensing camera system that comprises one or more fisheye lenses and infrared and/or near-infrared image sensors. In some examples, the image sensors may generate output signals based at least in part on receiving radiation via the fisheye lenses. A depth measurement may be calculated based at least in part on the output signals. For example, these output signals may be provided as input to a depth model, which may determine the depth measurement. In some examples, such a depth model may be integrated into an application-specific integrated circuit and/or may be operated by an application processor.
H04N 13/271 - Générateurs de signaux d’images où les signaux d’images générés comprennent des cartes de profondeur ou de disparité
H04N 13/254 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques en combinaison avec des sources de rayonnement électromagnétique pour l’éclairage du sujet
G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
H04N 5/33 - Transformation des rayonnements infrarouges
90.
Vehicle side view camera system with adjustable field of view
A vehicle can include one or more movably mounted cameras that are able to move to adjust a viewing angle of the camera(s) relative to a body of the vehicle. The camera(s) may be movable by virtue of being coupled to a movable side mirror, such that movement of the side mirror changes a field of view of the camera coupled to the side mirror. For example, the side mirror can be rotated about a rotational axis between a first position in which a field of view of the camera is directed in a first direction (e.g. toward a ground proximate the vehicle), and a second position in which the field of view of the camera is directed in a second direction, different than the first direction (e.g., outward from the vehicle or toward a door of the vehicle).
B60R 25/30 - Détection relative au vol ou autres événements relatifs aux systèmes antivol
B60R 1/12 - Ensembles de miroirs combinés avec d'autres objets, p.ex. pendules
B60R 1/07 - Dispositions pour les rétroviseurs montés à l'extérieur du véhicule avec une commande à distance pour régler leur position avec un organe actionné par l'énergie électrique
B60R 25/102 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme un signal étant envoyé vers un lieu distant, p.ex. signal radio transmis à un poste de police, à une entreprise de sécurité ou au propriétaire du véhicule
B60R 25/104 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme caractérisé par le type de signal antivol, p.ex. signaux visuels ou audibles ayant des caractéristiques spéciales
B60R 25/20 - Moyens pour enclencher ou arrêter le système antivol
B60R 25/10 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme
G02B 7/02 - Montures, moyens de réglage ou raccords étanches à la lumière pour éléments optiques pour lentilles
G02B 9/64 - Objectifs optiques caractérisés à la fois par le nombre de leurs composants et la façon dont ceux-ci sont disposés selon leur signe, c. à d. + ou — ayant plus de six composants
92.
Systems and methods for hybrid depth regularization
Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.
G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
G06T 7/44 - Analyse de la texture basée sur la description statistique de texture utilisant des opérateurs de l'image, p.ex. des filtres, des mesures de densité des bords ou des histogrammes locaux
The present invention relates to an image processing apparatus which determines an order for calculating output image pixels that maximally reuses data in a local memory for computing all relevant output image pixels. Thus, the same set of data is re-used until it is no longer necessary. Output image pixel locations are browsed to determine pixel values in an order imposed by available input data, rather than in an order imposed by pixel positions in the output image. Consequently, the amount of storage required for local memory as well as the number of input image read requests and data read from memory containing the input image is minimized.
A convolutional neural network (CNN) for an image processing system comprises an image cache responsive to a request to read a block of N×M pixels extending from a specified location within an input map to provide a block of N×M pixels at an output port. A convolution engine reads blocks of pixels from the output port, combines blocks of pixels with a corresponding set of weights to provide a product, and subjects the product to an activation function to provide an output pixel value. The image cache comprises a plurality of interleaved memories capable of simultaneously providing the N×M pixels at the output port in a single clock cycle. A controller provides a set of weights to the convolution engine before processing an input map, causes the convolution engine to scan across the input map by incrementing a specified location for successive blocks of pixels and generates an output map within the image cache by writing output pixel values to successive locations within the image cache.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system includes an image sensor, an adjustable aperture, and a memory. THE memory includes computer executable instructions that, when executed by a processor, cause the system to perform operations including obtaining a first image via the image sensor based at least in part on a first aperture stop of the adjustable aperture, identifying a first pixel of the first image, identifying a second pixel of the first image, determining a second aperture stop of the adjustable aperture based at least in part on the first pixel, determining a third aperture stop of the adjustable aperture based at least in part on the second pixel, obtaining a second image via the image sensor based at least in part on the second aperture stop, and obtaining a third image via the image sensor based at least in part on the third aperture stop.
An image processing method for iris recognition of a predetermined subject, comprises acquiring through an image sensor, a probe image illuminated by an infra-red (IR) illumination source, wherein the probe image comprises one or more eye regions and is overexposed until skin portions of the image are saturated. One or more iris regions are identified within the one or more eye regions of said probe image; and the identified iris regions are analysed to detect whether they belong to the predetermined subject.
H04N 5/33 - Transformation des rayonnements infrarouges
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images utilisant des procédés basés sur les caractéristiques
An apparatus for processing a neural network comprises an image memory into which an input image is written tile-by-tile, each tile overlapping a previous tile to a limited extent; a weights memory for storing weight information for a plurality of convolutional layers of a neural network, including at least two pooling layers; and a layer processing engine configured to combine information from the image and weights memories to generate an output map and to write the output map to image memory. The apparatus is configured to store a limited number of values from adjacent a boundary of an output map for a given layer. The layer processing engine is configured to combine the output map values from a previously processed image tile with the information from the image memory and the weights when generating an output map for a layer of the neural network following the given layer.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
99.
Systems and methods for encoding image files containing depth maps stored as metadata
Systems and methods in accordance with embodiments of the invention are configured to render images using light field image files containing an image synthesized from light field image data and metadata describing the image that includes a depth map. One embodiment of the invention includes a processor and memory containing a rendering application and a light field image file including an encoded image, a set of low resolution images, and metadata describing the encoded image, where the metadata comprises a depth map that specifies depths from the reference viewpoint for pixels in the encoded image. In addition, the rendering application configures the processor to: locate the encoded image within the light field image file; decode the encoded image; locate the metadata within the light field image file; and post process the decoded image by modifying the pixels based on the depths indicated within the depth map and the set of low resolution images to create a rendered image.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/36 - Prétraitement de l'image, c. à d. traitement de l'information image sans se préoccuper de l'identité de l'image
H04N 13/128 - Ajustement de la profondeur ou de la disparité
H04N 13/161 - Encodage, multiplexage ou démultiplexage de différentes composantes des signaux d’images
H04N 13/243 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques utilisant au moins trois capteurs d’images 2D
H04N 13/271 - Générateurs de signaux d’images où les signaux d’images générés comprennent des cartes de profondeur ou de disparité
G06T 7/50 - Récupération de la profondeur ou de la forme
G06T 9/20 - Codage des contours, p.ex. utilisant la détection des contours
H04N 19/597 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif spécialement adapté pour l’encodage de séquences vidéo multi-vues
H04N 19/625 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée utilisant une transformée en cosinus discrète
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
H04N 19/85 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo
A method is disclosed for processing at least a portion of an input digital image comprising rows of pixels extending in two mutually perpendicular directions over a 2D field. The method comprises defining a kernel for processing an image, the kernel comprising at least one row of contiguous elements of the same non-zero value (such rows being referred to herein as equal-valued kernel regions), the equal-valued kernel regions, if more than one, extending parallel to one another. For each pixel in at least selected parallel rows of pixels within the image portion, the cumulative sum of the pixel is calculated by adding a value of the pixel to the sum of all preceding pixel values in the same row of the image portion. The kernel is convolved with the image portion at successive kernel positions relative to the image portion such that each pixel in each selected row is a target pixel for a respective kernel position. For each kernel position, the convolving is performed, for each equal-valued kernel region, by calculating the difference between the cumulative sum of the pixel corresponding to the last element in the equal-valued kernel region and the cumulative sum of the pixel corresponding to the element immediately preceding the first element in the region, and summing the differences for all equal-valued kernel regions. The differences sum is scaled to provide a processed target pixel value.
