| Part I: Theory (2007) | |||||||||||||
Abstract | |||||||||||||
| This paper presents a stereo matching method which uses mul-tiple stereo pairs with various baselines to obtain precise distance estimates without suffering from ambiguity. In stereo ptvcessing. a shon baseline means that the estimated distance will be less precise due to namw triangulation. For more precise dismce estimation. a longer baseline is desired Wth a longer baseline. however. a larger dispariv range must be searched ro find a march. As a result, marching is more di’cult and there is a greater possibiliry of a false match. So there is a trade-off beween precision and accuracy in matching. The stereo matching method presented in this paper uses mul-tiple stereo pairs with different baselines generated by a lateral displacement of a camera Matching is performed simply bv com-puting the sum of squared-digerencc (SSD) values. The SSD func-tions for individual stereo pairs are represented with respect to the inverse distance (rather than the disparity as is usually done). and then are simply added 10 produce rhe sum of SSDs. This re-sulting function is called the SSSD-in-inrerse-distance. We show that the SSSD-in-inverse-distance~ction uhibiu a unique and clear minimum at the correct matching position even when the underlying inrensily patterns of the scene include ambiguities or repeiitive patterns. An advantage of this method is that we can eliminate false marches and increase precision without anv search or sequential filtering. This paper first defies a stereo algorithm based on the SSSD-in-inverse-distance and presents a marhemarical analysis 10 show how the algorithm can remove ambiguip and increusr precision. Then, a few experimental results with real siereo images are pre-senred to demonstrate the effectiveness of the algorithm I | |||||||||||||
Publication details | |||||||||||||
| |||||||||||||