30 November 2012 An image threshold selection method based on the Burr distribution
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It is important to accurately fit the unknown probability density functions of background or object. To solve this problem, the Burr distribution is introduced. Three-parameter Burr distribution can cover a wide range of distribution. The expectation maximization algorithm is used to deal with the estimation difficulty in the Burr distribution model. The expectation maximization algorithm starts from a set of selected appropriate parameters’ initial values, and then iterates the expectation-step and maximization-step until convergence to produce result parameters. The experiment results show that the Burr distribution could depicts quite successfully the probability density function of a significant class of image, and comparatively the method has low computing complexity.
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Xiaohong Xie, Xiaohong Xie, Rongteng Wu, Rongteng Wu, } "An image threshold selection method based on the Burr distribution", Proc. SPIE 8558, Optoelectronic Imaging and Multimedia Technology II, 85582K (30 November 2012); doi: 10.1117/12.2001143; https://doi.org/10.1117/12.2001143


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