Transactions of Nanjing University of Aeronautics & Astronautics
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Exploring a New Regularized Minimum Error Threshold Method
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Abstract:
To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory segmentation results only when the object and background of the image strictly obey a certain type of probability distribution, we propose the regularized minimum error threshold method and treat the traditional minimum error threshold method as its special case. Then we construct the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution. We investigate the impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method. To verify the effectiveness of our regularized minimum error threshold method, we select typical grey-scale images and perform their segmentation tests and compare the segmentation results obtained with our regularized minimum error threshold method with those obtained with the traditional minimum error threshold method respectively. The segmentation results, given in Figs. 2 through 4 and Table 1, and their analysis show that our regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method and does not exert much impact on object acquisition in case of the addition of a certain noise to an image. Therefore, our method can meet the requirements for extracting a real object in the noisy environment.
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