Asymptotically Admissible Texture Synthesis

Y.Q. Xu, S.C. Zhu, B.N. guo, and H.Y. Shum

Microsoft Research China and Ohio State Univ.

Recently there is a resurgent interest in example based texture analysis and synthesis in both computer vision and computer graphics. While study in computer vision is concerned with learning accurate texture models, research in graphics is aimed at effective algorithms for texture synthesis without necessarily obtaining explicit texture model. This paper makes three contributions to this recent excitment. First, we introduce a theoretical framework for designing and analyzing texture sampling algorithms. This framework, built upon the mathematical definition of texture, measures a texture sampling algorithm using admissibility, effectiveness, and sampling speed. Second, we compare and analyze texture sampling algorithms based on admissibility and effectiveness. In particular, we propose different design criteria for texture analysis algorithm in computer vision and texture synthesis algorithm in computer graphics. Finally, we develop a novel texture synthesis algorithm which samples from a subset of the Julesz ensemble by pasting texture patches from the observed texture. A ket feature of our algorithm is that it can synthesis high quality textures extremely fast. On a mid-level PC, we can synthesize a 512x512 pixel texture image from a 64x64 pixel example in just 0.03 second. This algorithm has been tested through extensive experiments and we report sampling results from our experiments.

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