Research Of Feature-based Image Stitching

Image stitching is an important research area of image processing. It is a technology that matches a series of images which are overlapped with each other and mixes these images into one image which has big field of view and has been widely used in the medical, industrial, aerospace and other fields.Because of difference of imaging system, point of view, the time as well as noise interference and obstructions, image stitching becames very difficult. According to the different basis of image registration, image stitching is divided into feature-based, gray-information-based and transformation-domain based. The feature-based image stitching is not only vulnerable to the light, rotation, but also helpful to increasing efficiency because the number of features is quite fewer than image piexel. Therefore, it become one type of most attended methods.This paper presents a robust image stitching algorithm.based on feature points.At first, the geometric basis of image stitching is introduced, and in accordance with the transformation between the two images of planar scene, projection transformation is choosed as transformation model. So, projection transformation is the basis of computation of transformation matrix and mixing images.The image stitching based on feature points is mainly composed of four steps: feature points extraction, matching feature points, computing transformation matrix and image mixing. Feature extraction is the first step in the process of image stitching, and is also very crucial. This article introduces and implements Harrris, SUSAN, SIFT three kinds of most popular feature point extraction algorithms. Because the SIFT algorithm is invariant to image translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection, SIFT descriptor has strong ability of matching. So the robust SIFT algorithm. is choosed as feature extraction algorithm. In stage of feature point matching, the k-d tree of SIFT features descriptor is build for two stitching images respectively, then use k-d tree to look for the nearest and the second nearest neighbor feature point in another image for each feature point in one image, and select initial matching point pairs. In the stage of computation of transformation matrix, PROSAC algorithm is used to filter the initial matching point pairs, and get the initial transformation matrix as well as inliers, then the L-M algorithm is applied to obtain higher precision transformation matrix. In the process of image mixing,in order to prevent the gray joints, gray of image is adjusted before mixing image based on the gray differences of inliers., then, according to transformation matrix, weighted mix images. Experiment results show that the quality of final stitching images.is well.