We have measured the performance of several area-based stereo matching algorithms with noise added to synthetic images. Dense disparity maps were computed and compared with the ground truth using three metrics: the fraction of correctly computed disparities, the mean and the standard deviation of the disparity error distribution. For a noise-free image, S. Birchfield and C. Tomasi's pixel-to-pixel dynamic algorithm performed slightly better than a simple sum-of-absolute-differences algorithm (67% correct matches vs 65%) $considered to be within experimental error. A census algorithm performed worst at only 54%. The dynamic algorithm performed well until the S/N ratio reached 36 dB after which its performance started to drop. However, with correctly chosen parameters, it was superior to correlation and census algorithms until the images became very noisy (/spl sim/15 dB). The dynamic algorithm also ran faster than the fastest correlation algorithms using an optimum window radius of 4, and more than 10 times faster than the census algorithm.
Robustness to noise of stereo matching
2003-01-01
381104 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Robustness to Noise of Stereo Matching
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