A stereo matching method estimates the disparity value between two correspondences in both stereo images. The disparity value represents the depth information of objects obtained from stereo images which have two different viewpoints. In many papers, the stereo matching method is tested under limited disparity conditions. These conditions follow configurations of test images. However, the disparity range in real applications is not known for those conditions and configurations. In this case, we have to check all pixels in the scan line to find the correspondence. Therefore, it is a time consuming task. Thus, we propose a fast disparity estimation method using the limited search range. The proposed method limits the disparity search range with the fast motion-search algorithm and local image characteristics.
Stereo matching methods estimate a disparity value of the object as depth information. In general, most stereo matching methods are tested under ideal radiometric conditions. However, those ideal conditions cannot exist in real life. Adaptive normalized cross correlation (ANCC) is a method that is robust to radiometric variation. It estimates significantly accurate disparity values in the illumination variant condition. However, it has a high complexity problem in the cost computation because of the block matching-based method and the bilateral filtering process. In this paper, we propose a pixel-based ANCC using hue and gradient information to improve the computation complexity problem. The results show that the cost computation time is reduced even though error rates corresponding to the exposure and illumination changes have larger variations than those of the ANCC result.