Global Techniques for Edge based Stereo Matching

Depth perception is one of the most active research areas in computer vision. Passive stereo vision is a well known technique for obtaining 3-D depth information of objects seen by two or more video cameras from different viewpoints (Hartley & Zisserman, 2000|Brown et al., 2003). The difference of the viewpoint positions causes a relative displacement of the corresponding features in the stereo images. Such relative displacement, called disparity, encodes the depth information, which is lost when the three dimensional scene is projected on an image. The key problem, which is difficult to solve and computationally expensive (Barnard & Fisher, 1982), is hence to compare each feature extracted from one image with a number, generally large, of features extracted from the other image in order to find the corresponding one, if any. Once the matching process is established and the stereo vision system parameters are known, the depth computation is reduced to a simple triangulation (Jane & Haubecker, 2000|Dooze, 2001). This chapter presents some recent research works proposed to solve the stereo matching problem. The presented methods are based on a global approach, which can be viewed as a constraint satisfaction problem where the objective is to highlight a solution for which the matches are as compatible as possible with respect to specific constraints. These methods are tested and evaluated for real-time obstacle detection in front of a vehicle using linear stereo vision.