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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