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This paper describes a method for establishing dense correspondence between two images in a video sequence (motion) or in a stereo pair (disparity) in case of large displacements. In order to deal with large-amplitude motion or disparity fields, multi-resolution techniques such as blocks matching and optical flow have been used in the past. Although quite successful, these techniques cannot easily cope with motion/disparity discontinuities as they do not explicitly exploit image structure. Additionally, their computational complexity is high; block matching requires examination of numerous vector candidates while optical flow-based techniques are iterative. In this paper, we propose a new approach that addresses both issues. The approach combines feature matching with Delaunay triangulation, and thus reliable long-range correspondences result while the computational complexity is not high (sparse representation). In the proposed approach, feature points are found first using a simple intensity corner detector. Then, correspondence pairs between two images are found by maximizing cross-correlation over a small window. Finally, the Delaunay triangulation is applied to the resulting points, and a dense vector field is computed by planar interpolation over Delaunay triangles. The resulting vector field is continuous everywhere, and thus does not reflect motion or depth discontinuities at object boundaries. In order to improve the rendition of such discontinuities, we propose to further divide Delaunay triangles whenever the displacement vectors within a triangle do not allow good intensity match. The approach has been extensively tested on stereoscopic images in the context of intermediate view reconstruction where the quality of estimated disparity fields is critical for final image rendering. The first results are very encouraging as the reconstructed images are of high quality, especially at object boundaries, and the computational complexity is lower than that of multi- resolution block matching.
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