Dense Stereo Matching Based on the Directional Local Binary Pattern

New applications such as 3D graphics, 3D displays, and image-based modeling have made stereo vision an active research area in recent years. In dense disparity map estimation, which is a basic problem in stereo vision, using two left and right images taken from a scene from two different positions, the disparity of each pixel of the reference image is determined (meaning determining each pixel with how displacement is appeared in the other image). Based on the disparity value, the depth of each pixel in scene is simply determined. For dense disparity map estimation, local stereo matching methods are simpler and faster than global methods, and therefore suitable for real time applications. In these methods, defining proper window which aggregate intensity pattern as well as keeping disparity consistency in all the window area, is an important challenge. To overcome this challenge, the idea of directional multiple window has been proposed in the previous researches. On the other hand, local binary patterns have considerable success in pattern recognition applications, while computationally simple. Therefore, the idea of using local binary pattern in a directional multiple window arrangement is proposed for dense stereo matching in this paper. Experimental results on standard stereo images show the better performance of the proposed method with respect to other proposed binary descriptors