Abstract: A computer-implemented stereo image processing method which uses contours is described. In an embodiment, contours are extracted from two silhouette images captured at substantially the same time by a stereo camera of at least part of an object in a scene. Stereo correspondences between contour points on corresponding scanlines in the two contour images (one corresponding to each silhouette image in the stereo pair) are calculated on the basis of contour point comparison metrics, such as the compatibility of the normal of the contours and/or a distance along the scanline between the point and a centroid of the contour. A corresponding system is also described.

Abstract: The subject disclosure is directed towards a high resolution, high frame rate, robust stereo depth system. The system provides depth data in varying conditions based upon stereo matching of images, including actively illuminated IR images in some implementations. A clean IR or RGB image may be captured and used with any other captured images in some implementations. Clean IR images may be obtained by using a notch filter to filter out the active illumination pattern. IR stereo cameras, a projector, broad spectrum IR LEDs and one or more other cameras may be incorporated into a single device, which may also include image processing components to internally compute depth data in the device for subsequent output.

Abstract: The subject disclosure is directed towards stereo matching based upon active illumination, including using a patch in a non-actively illuminated image to obtain weights that are used in patch similarity determinations in actively illuminated stereo images. To correlate pixels in actively illuminated stereo images, adaptive support weights computations may be used to determine similarity of patches corresponding to the pixels. In order to obtain meaningful adaptive support weights for the adaptive support weights computations, weights are obtained by processing a non-actively illuminated (“clean”) image.

Abstract: The subject disclosure is directed towards communicating image-related data between a base station and/or one or more satellite computing devices, e.g., tablet computers and/or smartphones. A satellite device captures image data and communicates image-related data (such as the images or depth data processed therefrom) to another device, such as a base station. The receiving device uses the image-related data to enhance depth data (e.g., a depth map) based upon the image data captured from the satellite device, which may be physically closer to something in the scene than the base station, for example. To more accurately capture depth data in various conditions, an active illumination pattern may be projected from the base station or another external projector, whereby satellite units may use the other source's active illumination and thereby need not consume internal power to benefit from active illumination.

Abstract: Real-time stereo matching is described, for example, to find depths of objects in an environment from an image capture device capturing a stream of stereo images of the objects. For example, the depths may be used to control augmented reality, robotics, natural user interface technology, gaming and other applications. Streams of stereo images, or single stereo images, obtained with or without patterns of illumination projected onto the environment are processed using a parallel-processing unit to obtain depth maps. In various embodiments a parallel-processing unit propagates values related to depth in rows or columns of a disparity map in parallel. In examples, the values may be propagated according to a measure of similarity between two images of a stereo pair; propagation may be temporal between disparity maps of frames of a stream of stereo images and may be spatial within a left or right disparity map.

Abstract: The description relates to stereo image matching to determine depth of a scene as captured by images. More specifically, the described implementations can involve a two-stage approach where the first stage can compute depth at highly accurate but sparse feature locations. The second stage can compute a dense depth map using the first stage as initialization. This improves accuracy and robustness of the dense depth map.