Statistics of patch offsets for image completion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Towards fast, generic video inpainting
Proceedings of the 10th European Conference on Visual Media Production
Special Section on CAD/Graphics 2013: Image compositing using dominant patch transformations
Computers and Graphics
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Matching patches between two images, also known as computing nearest-neighbor fields, has been proven a useful technique in various computer vision/graphics algorithms. But this is a computationally challenging nearest-neighbor search task, because both the query set and the candidate set are of image size. In this paper, we propose Propagation-Assisted KD-Trees to quickly compute an approximate solution. We develop a novel propagation search method for kd-trees. In this method the tree nodes checked by each query are propagated from the nearby queries. This method not only avoids the time-consuming backtracking in traditional tree methods, but is more accurate. Experiments on public data show that our method is 10–20 times faster than the PatchMatch method [4] at the same accuracy, or reduces its error by 70% at the same running time. Our method is also 2–5 times faster and is more accurate than Coherency Sensitive Hashing [22], a latest state-of-the-art method.