Probe into Image Segmentation Based on Sobel Operator and Maximum Entropy Algorithm

This paper focuses on the research of image segmentation accuracy problem because traditional Sobel operator image segmentation is easy to cause the vagueness of image segmentation, contrast is not apparent, segmentation accuracy is low. Directed against these defects, this paper puts forward an improved Sobel operator 2-d maximum entropy digital image segmentation method. This algorithm firstly carries out image segmentation, according to digital image characteristics, then finds its real edge through the threshold of Sobel edge detection algorithm. Finally applies the threshold value obtained from Sobel edge detection algorithm to 2-d maximum entropy image segmentation. Furthermore, this paper uses different threshold segmentation for digital image goals and objective fringes to solve the problem of segmentation inaccuracy as the result of the local adhesion and stack. Simulation experiments show that the proposed algorithm boasts of its good robustness for image segmentation, high segmentation rate, which has been proven to be an effective and applicable algorithm.