Improved Color Patch Similarity Measure Based Weighted Median Filter

Median filtering the intermediate flow fields during optimization has been demonstrated to be very useful for improving the estimation accuracy. By formulating the median filtering heuristic as non-local term in the objective function, and modifying the new term to include flow and image information that according to spatial distance, color similarity as well as the occlusion state, a weighted non-local term (a practical weighted median filter) reduces errors that are produced by median filtering and better preserves motion details. However, the color similarity measure, which is the most powerful cue, can be easily perturbed by noisy pixels. To increase robustness of the weighted median filter to noise, we introduce the idea of non-local patch denoising method to compute the color similarity in terms of patch difference. Most importantly, we propose an improved color patch similarity measure (ICPSM) to modify the traditional patch manner based measure from three aspects. Comparative experimental results on different optical flow benchmarks show that our method can denoise the flow

Improved Color Patch Similarity Measure Based Weighted Median Filter

Median filtering the intermediate flow fields during optimization has been demonstrated to be very useful for improving the estimation accuracy. By formulating the median filtering heuristic as non-local term in the objective function, and modifying the new term to include flow and image information that according to spatial distance, color similarity as well as the occlusion state, a weighted non-local term (a practical weighted median filter) reduces errors that are produced by median filtering and better preserves motion details. However, the color similarity measure, which is the most powerful cue, can be easily perturbed by noisy pixels. To increase robustness of the weighted median filter to noise, we introduce the idea of non-local patch denoising method to compute the color similarity in terms of patch difference. Most importantly, we propose an improved color patch similarity measure (ICPSM) to modify the traditional patch manner based measure from three aspects. Comparative experimental results on different optical flow benchmarks show that our method can denoise the flow

Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow

International audienceHandling all together large displacements, motion details and occlusions remains an open issue for reliable computation of optical flow in a video sequence. We propose a two-step aggregation paradigm to address this problem. The idea is to supply local motion candidates at every pixel in a first step, and then to combine them to determine the global optical flow field in a second step. We exploit local parametric estimations combined with patch correspondences and we experimentally demonstrate that they are sufficient to produce highly accurate motion candidates. The aggregation step is designed as the discrete optimization of a global regularized energy. The occlusion map is estimated jointly with the flow field throughout the two steps. We propose a generic exemplar-based approach for occlusion filling with motion vectors. We achieve state-of-the-art results in computer vision benchmarks, with particularly significant improvements in the case of large displacements and occlusions