Cooperative Stereo Matching with Color-Based Adaptive Local Support

Color processing imposes a new constraint on stereo vision algorithms: The assumption of constant color on object surfaces used to align local correlation windows with object boundaries has improved the accuracy of recent window based stereo algorithms significantly. While several algorithms have been presented that work with adaptive correlation windows defined by color similarity, only a few approaches use color based grouping to optimize initially computed traditional matching scores. This paper introduces the concept of color-dependent adaptive support weights to the definition of local support areas in cooperative stereo methods to improve the accuracy of depth estimation at object borders.