Region disparity estimation and object segmentation based on graph cut and combination of multiple features

Abstract

Moving objects segmentation is the foundation of intelligent moving body information collection. In the monocular vision system, it is too difficult to segment the foreground from background when their grayscale and color are similar. Compared with the grayscale and color, the scenery depth are less susceptible to external environment, if depth information can be got in stereo vision, it would be much easier to segment the foreground. Unfortunately, scenery accurate dense disparity map is often hard to get. In the paper, an optimizing method of calculating the dense disparity based on graph cut is used, and based on the dense disparity, the features of color and disparity are combined by graph cut to improve the accuracy of the image segmentation. The experimental results show that the method provides a better dense disparity map and also reduces the effect of light and strengthens the stability of segmentation by combining the features of color and disparity in graph cut optimization algorithm.