Research On The Algorithms Of Image Mosaic In Feature Points

Image mosaic is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot compared with a single image. Image mosaic is an important research field of image processing, and has widely applications in the fields of photogrammetry, computer vision, remote sensing image processing, medical image analysis, computer graphic and so on.This paper summarizes and analyzes the classification of the image stitching technology. Image mosaic algorithm is divided into three types:(1) The stitching algorithm based on region correlation;(2)The stitching algorithm based on gray;(3)The Feature-based stitching algorithm;In this paper, we mainly study the third type.Generally speaking, the image mosaic process consists of the following steps. Image acquisition, image registration, image fusion. Image registration is the important foundation of image mosaic. In this dissertation, the author mainly focuses on the research of feature point matching of the Harris feature points and the SIFT feature points. NCC method is adopted for the matching of Harris corners and Euclidean distance method is adopted for SIFT descriptor in the initial matching of the image key points.The Harris corner detection algorithm is widely applied in image mosaic, which is simple and stable. However, the algorithm has a disadvantage that it obtains a lot of false corners when there exist some noise in an image. An improved Harris corner detection algorithm is proposed in this paper. The new algorithm reduces the noise impact greatly. The experimental results show that the improved algorithm not only reduce false corner points greatly, but also retain the majority of true corners. As a result, it improves the detection accuracy and reduces the chance of error matching in image registration.In stage of feature point matching, the k-d tree of SIFT features descriptor is build for two stitching images respectively and select the initial matching points. Then filter the initial matching points by the method of Ransac and get the result of the transformation matrix. Finally, do the weighted fusion according to the derived transformation matrix. A large number of experiments show that, the result of mosaic is very well.