Research For Image Registration Based On Scale Invariant Feature Transform

Image registration is the process of trying to find the space transformation between two or more images of the same scene and transforming one or more images among them. Image registration is a crucial and basic step in all image anlysis tasks, it is the precondition of the application of image mosaic, image reconstruction, object recognition, to name a few. The common used method of image registration is the method based on feature, the key of the method is how to detect features more efficient. Scale invariant feature transform algorithm can provides us the invariant features that we want, scale invariant features have rotation, illuminance, affine and scale invariant, it is the most efficient algorithm of feature detection and matching.This paper taked the research of Scale Invariant Feature Transform algorithm as the center part. The first introduction part is the invariant feature theory as the background to introduce the conception of scale invariant feature transform. Scale invariant feature transform algorithm can be divided into three parts: features detection, features description and features matching. This paper analysed and discussed each parts of the algorithm, and gave a particular introduction on the implementation procedure of the scale invariant feature transform algorithm, including the researches on the character and performance of the algorithms like hough tranform, best bin first tree retrieval and random sample consensus algorithm and so on. At the step of feature detection, using Non-maximal suppression method to detect well-distributed features and reducing detection times by setting the flag to adjust detection step. Considering of the contraint of scale invariant feature transform algorithm when it set the parameter of the thresh of the distance ratio, the setting fix parameter don't satisfy all images, so it's necessary to find the best parameter of the thresh in the step of features matching. This paper designs a simple algorithm to find the optimization value based on binary search algorithm. For the points which have big error in the detection feature points, they can be eliminated by feature consistent geometry constrast between matching points. The experiments show the improved algorithm has better performance than the preview one.After the appearance of the scale invariant feature transform algorithm, on top of other algorithms to improve the scale invariant feature transform algorithm. This paper do some researches on these algorithms, and make some analysis and contrast experiments. From the contrast experiments, it can be found that these algorithms indeed make some progress in some parts of the algorithm, but they actually lost some other performance in other aspects, like reducing the using range of the algorithm, cutting down the scale invariant of the algorithm, or increasing the computation complexity of the algorithm. So it is still necessary to do some deep research on the algorithm. Now most of the job in improving and perfecting the algorithm are focusing on the aspects of improving the algorithm computing efficiency and providing more accurate feature algorithm or more available feature descriptor. Besides, the description part of the algorithm was inspired by biology nerve, the following research would continue take the biology principle into the improvement of the scale invariant feature transform algorithm. At the same time, it is the research key point to take the algorithm into the real life so as to solve more reality problems.