Research Of Characteristic Point Extraction And Matching On Digital Image

Image Matching is a fundamental task in image processing used to match two or more images, for example, at different time ,from different viewpoints, Registration is the foundation of many image processing and applications such as object recognition, change detection, 3-D modeling.The image matching method has very many kinds. Based on the image characteristic image registration is in the registration characteristic main aim at characteristic point. Characteristic point extraction is the image matching essential step. In this paper, the multianalysis has contrasted the Harris arithmetic operators, Moravec arithmetic operators, Plessey arithmetic operators, Forstner arithmetic operators and SUSAN arithmetic operators, from extraction efficiency, arithmetic operators stability, localization accuracy, Anti-noise, to carry on the analysis to compare to the operator with the test image to carry on the experimental analysis one by one. At the same time, a graph characteristic terrain feature centralized positioning method is put forward. The so-called graph characteristic terrain feature centralized positioning is a method, it draws support from the characteristic terrain feature whose two strings carries on the localization to the center of circle, we name it as the center of gravity localization. It is graph characteristic terrain feature centralized positioning innovation method.On the base of researching characteristic point extraction operator, the paper researches image matching based on characteristic point, Proposes corner point matching method based on the singular value decompound , and proposes one method based on the gradation mutual correlation matching-template matching method for contrasting , in the end, the paper analyses corner point matching method based on the singular value decompound through three principles of characteristic point matching arithmetic, educes experiment conclusion. The last chapter in the paper mainly carries on summary and forecast, summarizes the work of the paper, points out the good and bad points, proposes center of gravity of work in the future.