Study On The Method Of Image Segmentation Based On Ant Colony Algorithm

Ant colony algorithm, which was proposed in the 1990s, has been widely used in different areas. In this thesis, we deeply studied the ant colony algorithm and its application in image segmentation. The main contributions are as follows:Firstly, the ant colony algorithm is thorough studied. The basic principle, characteristics and application of the ant colony algorithm are discussed in detail. Especially, the application of ant colony algorithm in TSP is studied in detail and implemented by programming.Secondly, the basic principle of image segmentation is analyzed deeply. Several typical image segmentation methods are studied, including grey-level threshold method, edge detection method and region tracking method. The performance of each method is tested. Moreover, some recent image segmentation techniques are summarized.Furthermore, the method of image segmentation based on ant colony algorithm is studied. The segmentation model based on ant colony algorithm is established and the algorithm is implemented. Experimental results show the effectiveness of the proposed algorithm.Finally, a new image segmentation algorithm based on polymorphic ant colony algorithm is proposed. The basic ant colony based image segmentation algorithm has some problems, such as long searching time, large computation cost and so on. In order to address this problem, multiple ant colonies with local perception are distributed to achieve the entire perception of the image and complete the segmentation task cooperatively. Experimental results indicate that the proposed algorithm can segment the object rapidly and exactly.