Image Segmentation Based on 2-D Maximum Entropy and 2-D Minimum Cross Entropy

The thresholding method based 2-D maximum entropy and the one based on 2-D minimum cross entropy are used widely in image segmentation today,but in some applications,they fail to segment images because of too high or too low thresholds.Therefore,we proposed an image thresholding method based on the combination of 2-D maximum entropy and 2-D minimum cross entropy.Firstly,the formula of the 2-D minimum cross entropy was transformed,then 2-D maximum entropy and 2-D minimum cross entropy were combined together using multi-objective programming theory so that the optimal threshold value could satisfy the threshold requirement of the both.A new recursive algorithm was inferred using the features of the 2-D histogram in order to search the best threshold vector and to reduce the computing complexity.Experimental results show that: 1) the proposed method is effective and can compensate the shortcomings of the two methods in some applications;and 2) the computation time is less,which is about 0.3 second.