A distributed adaptive architecture for analog stereo depth estimation

We present an architectural description of a visual sensor capable of extracting a dense depth map of the scene under consideration. The goal of the stereo depth perception is twofold: find an estimation of distance for each point in the image and a confidence measure of this operation. Stereoscopic depth estimation is based on disparity measurements. At functional level, in the flow of processing, we distinguish: 1) adaptive convolution with Gabor fillers for measuring phase, 2) regularization process based on local context information, 3) a comparison between left and right phase maps to determine disparity. The architecture has been validated with respect to both artificial and real-world images