Local Binary Pattern and Census, Which One is Better in Stereo Matching

Local binary patterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have been successfully applied in various applications, such as texture classification, face recognition, object detection, and segmentation, while Census has only been used to investigate stereo correspondence problem. Therefore, this paper investigates the LBP and Census using a non-local-based stereo matching method in order to analyze and discuss the main differences between LBP and Census. Moreover, as many as one hundred variants of LBP have been published to solve various problems, while only a few modifications of the Census exist for stereo matching. Comprehensive experiments with the indoor, Middlebury dataset stated that some novel LBPs that perform well in texture classification and face recognition also work well in a stereo matching application. In most cases, LBP and its variants compare favorably to Census in terms of the accuracy of the stereo method. These results proved that LBP and its variants are suitable for using in solving the stereo correspondence problem or improving the performance of existing stereo methods.