Abstract
In this paper, we present a novel soft decision mixture model for image segmentation. This model adopts the soft decision classify into gaussian mixture model to represent the probability distribution of the observed image feature. The model for the underlying true context images is designed to serve as prior contextual constraints on unobserved pixel labels in term of markov random field model. Experiments with synthetic image and real image show that the use of soft decision mixture model definitely improves the quality of the segmentation results for noisy images and results in reduced classification errors in the interior area of the region.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lin, P., Zhang, F., Zheng, C., Yang, Y., Hou, Y. (2005). A Robust Soft Decision Mixture Model for Image Segmentation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_107
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DOI: https://doi.org/10.1007/11539902_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
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