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O. Veksler. Stereo correspondence by dynamic programming on a tree. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (2): 384–390, 2005. 19. L. Wang, M. Liao, M. Gong, R. Yang, and D. Nistér. High-quality real-time stereo using adaptive cost aggregation and dynamic programming. Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’06), 798–805, 2006. 20. Middlebury Stereo webpage. Q. Yang, L. Wang, R. Yang, H. Stewenius, and D.

52). 3 Semiglobal Matching In conventional global matching techniques, cost aggregation is done generally in a single matching direction, along the epipolar lines in the left or right image. However, with multiple matching directions, a more reliable disparity map can be obtained even using a simple matching algorithm. For example, Hirschmüller [7] proposes a global stereo matching technique called semiglobal matching (SGM). 12 shows multiple matching directions and a disparity space image in one of the directions.

The size of a cost volume is N × M × D, where N × M is the image resolution and D is the disparity range. Therefore, total (n + 1) × N × M × D memory space is needed to process a pair of stereo images. Computation time is therefore also increased with the increasing size of memory space. In this section, several stereo matching methods, including local and global methods, are briefly described. In addition, their matching performance is compared to help readers understand the difference in matching methods.

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Cavity Quantum Electrodynamics by S. Dutta

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