The aim of stereo matching is to find a corresponding point for each pixel in a reference image of a stereo image pair in the other image. Corresponding points are projections onto the stereo images of the same scene point. Finding corresponding points is an essential problem in dense stereo matching. The relative displacement between the corresponding points in rectified stereo images is termed disparity. Stereo matching is ambiguous because of photometric issues, surface structure and geometric ambiguities. Finding corresponding points within uniformly colored regions or surfaces with repeating texture or structure is a huge problem. Some points do not have corresponding points due to occlusion or due to the limited field of view. We defined a probabilistic framework for stereo matching using a one-dimensional hidden Markov model. We showed that the particle filter and the particle filter followed by smoothing can be used in disparity estimation. We introduced and qualitatively compared five probabilistic algorithms for disparity estimation: the forward algorithm, the forward/backward algorithm, the Viterbi algorithm, the particle filter and the particle filter in combination with smoothing. We derived a new likelihood function for correspondence that is optimal in a probabilistic sense. We deviated from the squared window based likelihood in order to include only relevant pixels in the likelihood function. We introduced local stereo matching using sparse windows. This approach gave us a significant improvement compared to matching based on the complete windows. Further led by the idea that a different nature of texture requires a different approach to likelihood estimation, we redefined several of the most common assumptions and established a relationship between the texture and the fronto-parallel assumption and introduced local adaptive segmentation based on the local intensity variation. We redefined the Lambertian assumption for offset compensation and introduced novel preprocessing and postprocessing steps for accurate disparity map estimation. We demonstrated the performance of our algorithm on benchmark images from the Middlebury database and on own examples, and showed that the disparity maps of scenes of different natures are successfully estimated.
|Award date||8 Nov 2012|
|Place of Publication||Enschede|
|Publication status||Published - 8 Nov 2012|
- stereo matching
- similarity measure