Showing papers by "Kaiming He published in 2012"

Statistics of Patch Offsets for Image Completion

Abstract: Image completion involves filling missing parts in images. In this paper we address this problem through the statistics of patch offsets. We observe that if we match similar patches in the image and obtain their offsets (relative positions), the statistics of these offsets are sparsely distributed. We further observe that a few dominant offsets provide reliable information for completing the image. With these offsets we fill the missing region by combining a stack of shifted images via optimization. A variety of experiments show that our method yields generally better results and is faster than existing state-of-the-art methods.

Computing nearest-neighbor fields via Propagation-Assisted KD-Trees

Abstract: 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.