Abstract
We present a new approach for texture modeling, which joins two ideas: well defined patterns used as ”elementary texture elements” and statistical modeling based on Gibbs probability distributions. The developed model is useful for a wide range of textures. Within the scope of the method it is possible to pose such tasks as e.g. learning the parameters of the prior model, texture synthesis and texture segmentation in a very natural and good founded way. To solve these tasks we propose approximative schemes based on the Gibbs Sampler combined with the Expectation Maximization algorithm for learning. Preliminary experiments show good performance and accuracy of the method.
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Schlesinger, D. (2006). Template Based Gibbs Probability Distributions for Texture Modeling and Segmentation. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_4
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DOI: https://doi.org/10.1007/11861898_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
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