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Proceedings Paper

Texture synthesis based on cluster transition probabilities
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Paper Abstract

This paper introduces an approach for synthesizing natural textures. Textures are modeled using a block-transition probabilistic model. In the training phase, the original textured image is split into equal size blocks, and clustered using the k-means clustering algorithm. Then, the transition probabilities between block-clusters are calculated. In the synthesis phase, the algorithm generates a sequence of indices, each representing a block-cluster, based on the transition probabilities. One advantage of this method over previous block sampling techniques is its stability. More specifically, the texture is synthesized block-by-block in a raster order. The block at a specific location is selected from one of the original image blocks. Thus, synthesis does not lead to artifacts. Additionally, the algorithm uses pre- and post- filtering. The image is filtered by a predictive filter, and the residual image is modeled using the probabilistic approach. The final synthesized image is the result of filtering the residual image by the inverse filter. Using pre- and post- processing eliminates the blockage effect. Moreover, the algorithm is computationally inexpensive, and the synthesis phase is particularly fast since it only requires generation of a sequence of cluster indices. Results show that the proposed method is successful in synthesizing realistic natural textures for a large variety of textures.

Paper Details

Date Published: 8 August 2003
PDF: 12 pages
Proc. SPIE 5108, Visual Information Processing XII, (8 August 2003); doi: 10.1117/12.487850
Show Author Affiliations
Dimitrios Charalampidis, Univ. of New Orleans (United States)


Published in SPIE Proceedings Vol. 5108:
Visual Information Processing XII
Zeno J. Geradts; Zia-ur Rahman; Lenny I. Rudin; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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