文章基本信息

标题：GPU-based SoftAssign for Maximizing Image Utilization in Photomosaics
其他标题：GPU-based SoftAssign for Maximizing Image Utilization in Photomosaics
本地全文：下载
作者：Marcos Slomp ; Michihiro Mikamo ; Bisser Raytchev 等
期刊名称：International Journal of Networking and Computing
印刷版ISSN：2185-2847
出版年度：2011
卷号：1
期号：2
页码：211-299
语种：English
出版社：International Journal of Networking and Computing
摘要：Photomosaic generation is a popular non-photorealistic rendering technique, where a single image is assembled from several smaller ones. Visual responses change depending on the proximity to the photomosaic, leading to many creative prospects for publicity and art. Synthesizing photomosaics typically requires very large image databases in order to produce pleasing results. Moreover, repetitions are allowed to occur which may locally bias the mosaic. This paper provides alternatives to prevent repetitions while still being robust enough to work with coarse image subsets. Three approaches were considered for the matching stage of photomosaics: a greedy-based procedural algorithm, simulated annealing and SoftAssign. It was found that the latter delivers adequate arrangements in cases where only a restricted number of images is available. This paper introduces a novel GPU-accelerated SoftAssign implementation that outperforms an optimized CPU implementation by a factor of 60 times in the tested hardware.
关键词：Non-photorealistic rendering (NPR); Photomosaic; SoftAssign; Simulated Annealing; General-Purpose GPU programming (GPGPU)