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Gaussian Scale-space Dense Disparity Estimation with Anisotropic Disparity-field Diffusion
Citation key 0745Kim2005
Author Jang-Heon Kim and Thomas Sikora
Title of Book IEEE Int. Conf. on 3-D Digital Imaging and Modeling (3DIM '05)
Year 2005
Address Ottawa, Ontario, Canada
Month jun
Abstract We present a new reliable dense disparity estimation algorithm which employs Gaussian scale-space with anisotropic disparity-field diffusion. This algorithm estimates edge-preserving dense disparity vectors using a diffusive method on iteratively Gaussianfiltered images with a scale, i.e. the Gaussian scalespace.While a Gaussian filter kernel generates a coarser resolution from stereo image pairs, only strong and meaningful boundaries are adaptively selected on the resolution of the filtered images. Then, coarse global disparity vectors are initialized using the boundary constraint. The per-pixel disparity vectors are iteratively obtained by the local adjustment of the global disparity vectors using an energy-minimization framework. The proposed algorithm preserves the boundaries while inner regions are smoothed using anisotropic disparity-field diffusion.In this work, the Gaussian scale-space efficiently avoids illegal matching on a large baseline by the restriction of the range. Moreover, it prevents the computation from iterating into local minima of illposed diffusion on large gradient areas e.g. shadow and texture region, etc. The experimental results prove the excellent localization performance preserving the disparity discontinuity of each object.
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