Disparity Map Computation on a Cell Processor

This report describes an efficient implementation of dynamic programming and belief propagation algorithms on a cell processor that may be used to speed up stereo image analysis. Dynamic programming is a method for efficiently solving optimization problems by caching subproblem solutions rather than recomputing them again. It processes image data by scanline optimization; thus it is easily implemented on a cell processor; our results show that this algorithm runs very efficiently on the cell processor. Belief propagation differs from dynamic programming by having potentially the whole image area as an area of influence for every pixel; this global optimization scheme produces improved results, but requires more run time than the dynamic programming method on a normal PC. Besides the tests on synthetic data, we use real-world image sequences captured by a test vehicle (HAKA1); they are typically degraded by various types of noise, changes in lighting, differing exposures, and so on. We use two methods to process the original images: Sobel edge detection and residual image analysis. Our results show that a cell processor also reduces running time for these processes. Sobel and residual images can improve the stereo matching result compared to the use of original real-world images, however, due to the used block structure and cell architecture limitations, the accuracy is also degraded slightly.