9 November 2010 An image threshold estimation model
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Abstract
The Guassian distribution model is often used to characterize the statistical behavior of image or other multimedia signal, and applied in fitting probability density functions of a signal. But, in practically, the probability density function of data source may be inherently non-Gaussian. As the distribution family covers most of the common distribution types and the frequency curves provided by the family are as wide as in general use, this paper considers Johnson distribution family to estimate the unknown parameters and approximate the empirical distribution. The method uses the moments to initialize the parameters of the distribution family, and then calculates parameters by using EM algorithm. The experiment results show that the fitted model could depicts quite successfully the both Gaussian and non-Gaussian probability density function of image intensity, and comparatively the method has low computing complexity.
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Rongteng Wu, Rongteng Wu, Xiaohong Xie, Xiaohong Xie, Zeyun Song, Zeyun Song, } "An image threshold estimation model", Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 78500A (9 November 2010); doi: 10.1117/12.871813; https://doi.org/10.1117/12.871813
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