Real-Time Stereo Vision Applications

Abstract

Depth perception is one of the important tasks of a computer vision system. Stereo correspondence by calculating the distance of various points in a scene relative to the position of a camera allows the performance of complex tasks, such as depth measurements and environment reconstruction (Jain et al., 1995). The most common approach for extracting depth information from intensity images is by means of a stereo camera setup. The point-by-point matching between the two images from the stereo setup derives the depth images, or the so called disparity maps, (Faugeras, 1993). The computational demanding task of matching can be reduced to a one dimensional search, only by accurately rectified stereo pairs in which horizontal scan lines reside on the same epipolar plane, as shown in Figure 1. By definition, the epipolar plane is defined by the point P and the two camera optical centers OL and OR. This plane POLOR intersects the two image planes at lines EP1 and EP2, which are called epipolar lines . Line EP1 is passing through two points: EL and PL, and line EP2 is passing through ER and PR respectively. EL and ER are called epipolar points and are the intersection points of the baseline OLOR with each of the image planes. The computational significance for matching different views is that for a point in the first image, its corresponding point in the second image must lie on the epipolar line, and thus the search space for a correspondence is reduced from 2 dimensions to 1 dimension. This is called the epipolar constraint. The difference on the horizontal coordinates of points PL and PR is the disparity. The disparity map consists of all disparity values of the image. Having extracted the disparity map, problems such as 3D reconstruction, positioning, mobile robot navigation, obstacle avoidance, etc, can be dealt with in a more efficient way (Murray & Jennings, 1997; Murray & Little, 2000).