A Kind of Two-Dimensional Entropic Image Segmentation Method Based on Artificial Immune Algorithm
Youxin Li, Zongyuan Mao, Lianfang Tian, Guangxing Tan · 2006
Two-dimensional entropic segmentation method has been greatly developed because of high segmentation accuracy and good stability, while a hard problem is that it gives rise to the exponential increment of computational time in comparison with the traditional one-dimensional histogram partition technology. To solve this problem, a new kind of image thresholding method is presented based on the combination of the artificial immune algorithm (AIA) and two-dimensional entropy techniques in this paper. This method can effectively improve the computation time and avoid getting into local optimization of the threshold by making use of AIA's characteristics of the intelligent computation, adaptive evolution and globally optimizing. The test results show that the method is effective and practicable.