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Deformable Spatial Pyramid Matching for Fast Dense Correspondences
lib:063782fe41cda2ff (v1.0.0)
Authors: Jaechul Kim,Ce Liu,Fei Sha,Kristen Grauman
Where published: CVPR 2013 6
Document: PDF DOI
Abstract URL: http://openaccess.thecvf.com/content_cvpr_2013/html/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.html
We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel level, we propose a pyramid graph model that simultaneously regularizes match consistency at multiple spatial extents--ranging from an entire image, to coarse grid cells, to every single pixel. This novel regularization substantially improves pixel-level matching in the face of challenging image variations, while the "deformable" aspect of our model overcomes the strict rigidity of traditional spatial pyramids. Results on LabelMe and Caltech show our approach outperforms state-of-the-art methods (SIFT Flow [15] and PatchMatch [2]), both in terms of accuracy and run time.
Where published: CVPR 2013 6
Document: PDF DOI
Abstract URL: http://openaccess.thecvf.com/content_cvpr_2013/html/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.html
We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel level, we propose a pyramid graph model that simultaneously regularizes match consistency at multiple spatial extents--ranging from an entire image, to coarse grid cells, to every single pixel. This novel regularization substantially improves pixel-level matching in the face of challenging image variations, while the "deformable" aspect of our model overcomes the strict rigidity of traditional spatial pyramids. Results on LabelMe and Caltech show our approach outperforms state-of-the-art methods (SIFT Flow [15] and PatchMatch [2]), both in terms of accuracy and run time.
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