Study On Algorithms For Image Segmentation And Their Implementation

Image Segmentation is the technique and the process to segment an image into different sub-images with different characters and to extract the interested objects from the image. It is an important and basic procedure in the field of computer vision, especially a key technique in such field as image processing, image analyzing and image understanding. The quality of image segmentation directly affects the performance of vision system. Therefore, the research into image segmentation has always been one of the central issues for researchers in the field of image process, which results in an amount of algorithms for image segmentation. And these algorithms have been put into practice broadly in areas like computer vision, pattern recognition, medical image processing, etc.So far, although many algorithms for image segmentation have emerged, there is no general one. Most of the algorithms are intended for some specific images. In the research of algorithms for image segmentation, the problem that often needs most consideration is the suppression against the numerous interference noises in real images and the improvement of calculation speed, which is also a tough problem in image segmentation. Considering this problem, we did some research into some traditional and classical algorithms, and provided some possible improvements. Firstly, this paper researches into traditional threshold segmentation algorithm, put forward a fast Otsu algorithm for threshold segmentation based on two-dimensional histogram, and an algorithm for post-processing by fuzzy entropy. Using these methods, we fully considered the spatial information of image neighborhood, transferred the original 2D threshold into 1D threshold, and furthermore introduced fast algorithm, reducing computation greatly, and thus improving the efficiency of image segmentation. At the same time, we proved the feasibility and high effectiveness of these methods through some experiments. These methods are prior to the traditional 2D Otsu method either in terms of noise suppression or in terms of operation speed. Secondly, in this paper, we did further research into edge detection algorithm. Because traditional edge detection method for differential operators cannot give consideration to both anti-noise and edge pinpoint, this paper put forward a multi-scale edge detection method based on wavelet transform, which can suppress a large amount of noises. And this paper particularly came up with an appropriate combination of the thought of multi-scale wavelet transform and classical operator, with which the edge can be detected very well. This method is especially effective in segmentation for images with a large amount of noises. Lastly, this paper also did some research on texture image. Texture image segmentation algorithm based on texture energy features and FCM has been put forward on the basis of understanding about texture energy features, and has been realized through experiments. The result of this segmentation method has been discussed, and some conclusions useful to further segmentation of texture images have been reached, which laid the foundation for more detailed study on texture image segmentation.