Dense Disparity Estimation using Stereo Images

Abstract

quot Stereo vision refers to the ability to infer information on the three-dimensional (3D) structure and distance/depth of a scene using two images captured from different view-points. It imitates one of the tasks performed by the human brain and the two eyes. In the stereo vision, a scene point is projected onto different locations on the two image planes (left and right cameras) and the main goal here is to find the orresponding pixels i.e., pixels resulting from the projection of the same 3D point onto the two image planes. The displacement between corresponding pixels is called disparity , and obtaining the same at each pixel location forms adense disparity map. However, estimation of disparities is an ill-posed problem and hence in practice is solved by formulating it as a global energy minimization problem. An energy function represents a combination of a data term and a prior term that restricts the solution space, and choosing a suitable data as well as prior models lead to accurate dense disparity estimates. In this thesis, we address this problem of dense disparity map estimation using rectified stereo images with known calibration of cameras and propose various approaches for solving it in a global energy minimization framework. We utilize graph cuts , an efficient and fast optimization technique for minimizing our energy functions.We first propose a method for dense disparity estimation using inhomogeneous Gaussian Markov random field (IGMRF) prior where we model the disparity map using this prior. The estimated IGMRF parameters assist us to yield a smooth solution while preserving the sharp depth discontinuities. In order to model the data term, we use the pixel-based intensity matching cost which is based on the brightness constancy assumption of the corresponding pixels. A learning based approach is used to obtain an initial disparity map which is used in obtaining the IGMRF parameters. The dense disparity map is obtained by minimizing the energy function using graph cuts.