Disparity estimation by stereo using particle swarm optimization and graph cuts
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Stereo vision is based on the process of obtaining the disparity from a left and a right view of a scene. By obtaining the disparity, we find the distance (depth) of each object point from the camera so that we can construct a 3-D form of a scene. A disparity map indicates the depth of the scene at various points. In this thesis we first discuss the local window based approaches like correlation window and adaptive window for finding the disparity map. These local approaches perform well in highly textured regions, non repetitive and in irregular patterns. However they produce noisy disparities in texture less region and fail to account for occluded areas. We then discuss the particle swarm optimization and graph cuts as global optimization techniques as the tools to obtain better estimates for the disparity map. These algorithms make smoothness assumption explicitly and solve the problem by minimizing the specified energy function. Particle swarm optimization, a bio inspired optimization technique is simple to implement but has high time complexity whereas graph cuts converges very fast yielding better estimates. In this thesis, we use rectified stereo pairs. This reduces the correspondence search to 1-D. To demonstrate the effectiveness of the algorithms, the experimental results from the stereo pairs including the ones with ground truth values for quantitative comparison is presented. Our results show that the disparity estimated using the graph cuts minimization performs better than the particle swarm optimization and local window based approaches in terms of quantitative measures with fast convergence.
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