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PatchTable: Efficient Patch Queries for Large Datasets and Applications

By Connelly Barnes, Fang-lue Zhang, Liming Lou and Xian Wu Shi-min Hu


example in several different styles (b-d). We show a detailed crop region of a single frame of the full video, which is shown in the supplemental video. In the second row we show the exemplar pair that is used to drive the stylization, which consists of an image “before ” and “after” the effect is applied. Our data structure enables this effect to be rendered on the CPU at 1024x576 resolution at 1 frame/second, which is significantly faster than previous work. Credits: Video in top row © Renars Vilnis; (b) Thomas Nast; (c) Vincent Van Gogh; (d) © Hashimoto et al. [2003]. This paper presents a data structure that reduces approximate near-est neighbor query times for image patches in large datasets. Pre-vious work in texture synthesis has demonstrated real-time syn-thesis from small exemplar textures. However, high performance has proved elusive for modern patch-based optimization techniques which frequently use many exemplar images in the tens of megapix-els or above. Our new algorithm, PatchTable, offloads as much of the computation as possible to a pre-computation stage that takes modest time, so patch queries can be as efficient as possi-ble. There are three key insights behind our algorithm: (1) a looku

Topics: CR Categories, I.3.6 [Computing Methodologies, Com- puter Graphics—Methodology and Techniques, I.4.9 [Comput- in
Year: 2016
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