The Analysis of the Image Segmentation Aimed for Sense Matching

摘要

Image segmentation technique is the foundation of the high-level digital image processing, and it is widely applied in many areas, it is also a classic difficult problem on the domain of advanced information processing. Because of its' importance and difficulties, image segmentation processing motivates large numbers of researchers to work for it, and quite a number of segmentation thoughts and algorithms have been proposed over the years. But by far, it is very difficult to provide a general effective segmentation algorithm, and further more there is no objective criteria to value the performance of the image segmentation algorithms. From the theory research of the image segmentation, there are two main directions:1). To improve the classic segmentation algorithms. 2). To provide the novel thought and novel approaches. From the application of the image segmentation. it is mainly applied for ATRs(Automatic Target Recognition system) and industry testing. Because the segmentation cannot only greatly compress data and reduce the required memory space but also simplify the analysis and the following processing steps. In this paper, we propose a new application of the image segmentation―image matching in real-time systems. And the key problem is that we need provide the "best" segmentation methods which are suitable to the following matching processing. The rule to select the segmentation method is also provided. Using the traditional matching scheme, the experimental results show that performance of the segmentation algorithm(2-D OTSU) is unstable, and the correct matching probability (CMP) is increased rapidly when the size of real images(matching basic unit) become larger and larger, compared with the tradition methods, some of them are better than the direct gray-level image matching when the real image becomes large, but the average CMP(ACMP) is not good. To improve the ACMP, we provide a new method for image matching ___combined matching. We make every three sequential images a group, and make this group as the matching basic unit.