Abstract
Stereo vision is the one of the major researched domains of computer vision, and it can be used for different applications, among them, extraction of the depth of a scene. This project provides a review of stereo vision and matching algorithms, used to solve the correspondence problem [22]. A stereo vision system consists of two cameras with parallel optical axes which are separated from each other by a small distance. The system is used to produce two stereoscopic pictures of a given object. Distance estimation between the object and the cameras depends on two factors, the distance between the positions of the object in the pictures and the focal lengths of the cameras [37]. In this project, the internal parameters of the cameras are calculated after taking several pictures from both left and right cameras. The correspondence problem between left and right images is solved using MATLAB. The disparity is then estimated based on the center of the image plane and finally, the distance of the object is calculated using the epipolar triangulation method. The results of this project showed that the distance estimated using stereo vision to ten different objects is relatively accurate. Two tests are completed in this project. The first one is done for checking the error between manual and stereo vision results, where the error ranged from 0.09% to 1.4%, the second test is done for comparison between passive and active stereo vision methods, this project used the passive method and the average error was equal to 0.78% which is less than the other methods.