Choosing local matching score method for stereo matching based-on polarization imaging

Polarization imaging is a powerful tool to observe hidden information from an observed object. It has significant advantages, such as computational efficiency (it only needs gray scale images) and can be easily applied by adding a polarizer in front of a camera. Many researchers used polarization in various areas of computer vision, such as object recognition, segmentation and so on. However, there is very little research in stereo vision based on polarization. Stereo vision is a well known technique for obtaining depth information from pairs of stereo digital images. One of the main focuses of research in this area is to get accurate stereo correspondences. In our work, we will study and develop a stereoscopic prototype that combines stereo vision and polarization imaging. This paper presents improved results in image acquisition, scene analysis, polarimetric calibration, and finding the best local matches between two stereo images.