Abstract
This paper presents a novel texture segmentation method using Bayesian estimation and neural networks. Multi-scale wavelet coefficients and the context information extracted from neighboring wavelet coefficients were used as input for the neural networks. The output was modeled as a posterior probability. The context information was obtained by HMT (Hidden Markov Trees) model. The proposed segmentation method shows performed better than ML (Maximum Likelihood) segmentation using the HMT model.
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Kim, T.H., Eom, I.K., Kim, Y.S. (2005). Texture Segmentation Using Neural Networks and Multi-scale Wavelet Features. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_59
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DOI: https://doi.org/10.1007/11539117_59
Publisher Name: Springer, Berlin, Heidelberg
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