Research On Mathematical Morphology Based On The Two-dimensional Maximum Entropy And Canny Edge Detection

With the development of computer technology,especially the theory of multimeidia and digital image processing and analysis in application areas become more and more widely, the image is more direct richer information carrier, already has become human being gaining and making use of important information sources and means.Image is becoming the more and more important object of study. Image segmentation is one important domain of digital image processing, and is also one of the domestic and international concerns on research issue at present. It has great significance to the image feature extraction, image analysis and recognition, computer vision and so on. However, due to the difference of image segmentation, image segmentation has been the most attractive and difficult problem in the digital image processing.It is a new way to process and analyze image signals using Mathematical morphology theories. As an non-linear method, it avoides the drawbacks of the traditional means from numerical modeling and analyzing and takes advantages of the processes from the way of collection and analyze. It has made an integrated system composed oftheories, methods and algorithms.In this paper, image segmentation algorithms are researched based on mathematical morphology. It discusseses the concept of mathematical morphology, mathematical morphology operators and mathematical morphology of method, and is applied to image segmentation and then presents new image segmentation algorithm of mathematical morphology based on the Two-dimensional maximum entropy and Canny edge detection:First of all, it detects the image by canny edge detection, takes the received segmentation information as necessary reference information, and defines parameters based on region-based segmentation algorithm. Then you can rapidly and efficiently search for the best threshold with the algorithm of mathematical morphology based on the Two-dimensional maximum entropy therefore to handle the edge image, finally carry out the edge connection.. The experiment indicates that it can not only maintain the image of maximum entropy, but also to maintain the integrity of the image edge and the detected edges are all single-pixel line. It remains the image information at most while picks the single-pixel edge quickly and precisely.