Abstract:
A technique is provided for recognizing faces in an image stream using a digital image acquisition device. A first acquired image is received from an image stream. A first face region is detected within the first acquired image having a given size and a respective location within the first acquired image. First faceprint data uniquely identifying the first face region are extracted along with first peripheral region data around the first face region. The first faceprint and peripheral region data are stored, and the first peripheral region data are associated with the first face region. The first face region is tracked until a face lock is lost. A second face region is detected within a second acquired image from the image stream. Second peripheral region data around the second face region are extracted. The second face region is identified upon matching the first and second peripheral region data.
Abstract:
An implementation efficient method of distinguishing between foreground and background regions of a digital image of a scene comprises capturing two images of nominally the same scene and storing the captured images in DCT-coded format, the first image being taken with the foreground more in focus than the background and the second image being taken with the background more in focus than the foreground. Regions of the first image are assigned as foreground or background according to whether the sum of selected higher order DCT coefficients decreases or increases for the equivalent regions of the second image.
Abstract:
A method for providing improved foreground / background separation in a digital image of a scene is disclosed. The method comprises providing a first map comprising one or more regions provisionally defined as one of foreground or background within the digital image; and providing a subject profile corresponding to a region of interest of the digital image. The provisionally defined regions are compared with the subject profile to determine if any of the regions intersect with the profile region. The definition of one or more of the regions in the map is changed based on the comparison.
Abstract:
An implementation efficient method of distinguishing between foreground and background regions of a digital image of a scene comprises capturing two images of nominally the same scene and storing the captured images in DCT-coded format, the first image being taken with the foreground more in focus than the background and the second image being taken with the background more in focus than the foreground. Regions of the first image are assigned as foreground or background according to whether the sum of selected higher order DCT coefficients decreases or increases for the equivalent regions of the second image.
Abstract:
A dynamically reconfigurable heterogeneous systolic array is configured to process a first image frame, and to generate image processing primatives from the image frame, and to store the primatives and the corresponding image frame in a memory store. A characteristic of the image frame is determined. Based on the characteristic, the array is reconfigured to process a following image frame.
Abstract:
An image acquisition device having a wide field of view includes a lens and image sensor configured to capture an original wide field of view (WFoV) image with a field of view of more than 90°. The device has an object detection engine that includes one or more cascades of object classifiers, e.g., face classifiers. A WFoV correction engine may apply rectilinear and/or cylindrical projections to pixels of the WFoV image, and/or non-linear, rectilinear and/or cylindrical lens elements or lens portions serve to prevent and/or correct distortion within the original WFoV image. One or more objects located within the original and/or distortion-corrected WFoV image is/are detectable by the object detection engine upon application of the one or more cascades of object classifiers.
Abstract:
A method of combining image data from multiple frames to enhance one or more parameters of digital image quality, e.g., video or still images, includes acquiring a first image at a first exposure duration, as well as acquiring a second image at a second exposure duration shorter than the first exposure duration and at a time just before, just after or overlapping in time with acquiring the first image, such that the first and second images include approximately a same first scene. In this way, the second image is relatively sharp and under-exposed, while the first image is relatively well-exposed and less sharp than the second image. Brightness and/or color information are extracted from the first image and applied to the second image to generate an enhanced version of the second image.
Abstract:
A face recognition technique includes using a multi-classifier face detector to determine above a threshold probability that region of an image includes a face. Further probability values are determined for a set of classifiers for the region to provide a recognition profile. Face detection and recognition probabilities are determined for at least one classifier of the set. The recognition profile is compared against a predetermined recognition profile to determine a degree of match.
Abstract:
A face recognition technique includes using a multi-classifier face detector to determine above a threshold probability that region of an image includes a face. Further probability values are determined for a set of classifiers for the region to provide a recognition profile. Face detection and recognition probabilities are determined for at least one classifier of the set. The recognition profile is compared against a predetermined recognition profile to determine a degree of match.
Abstract:
A technique for processing a digital image uses face detection to achieve one or more desired image processing parameters. A group of pixels is identified that corresponds to a face image within the digital image. A skin tone is detected for the face image by determining one or more default color or tonal values, or combinations thereof, for the group of pixels. Values of one or more parameters are adjusted for the group of pixels that correspond to the face image based on the detected skin tone.