Enhancement of calibrated stereo vision depth map estimation for autostereoscopic viewing

In this paper, we describe a new algorithm to enhance the calibrated stereo camera based depth map (disparity) estimation to view the 3D scene content on autostereoscopic displays. There are various similarity measure methods for calculating the disparity between a block in the first image and the corresponding block in the second image that is most similar to the block in the first image. This process is also called template matching. In the case of rectified stereo images, the template matching algorithm compares the template block from the first image against all blocks along the epipolar line in the second image assuming that all the corresponding pixels of the template block displaced by the same amount. The proposed algorithm employs the same principle, but differs with its similarity measure algorithm which is noise robust as well as intensity-wise shift and scale invariant. A comparative evaluation between the normalized cross-correlation based similarity measure and the proposed algorithm are given in the paper.