Research On Image Stitching Technology Based On Partition Detection And Improved RANSAC Algorithm

Since the 21 st century,with the progress and continuous development of science and technology,the science and technology of computer vision has showed its vitality.Based on the needs of society,image stitching technology has become an essential role in the field of digital image processing.Over the time,image stitching technology has increasingly gained its popularity and it has had its own significance in many fields such as aerospace,civil and military drones,medical image identification,and the satellite navigation which we often use.As we know,if an image contains more things,its resolution for a specific detail will be reduced.Image stitching technology is born to deal with this contradiction.This paper proposes improvements and puts them into experiment,aiming at some shortcomings in the process of existing image stitching algorithms.Based on the study and research of image stitching technology,the improvements proposed in this article are mainly demonstrated in the following aspects:(1)Propose an improved weighted and increased rate RANSAC algorithm(1)The selection of sample points is random every time,in the principle and process of the original RANSAC method.In this way,the probability of the outer point and the inner point being selected for the calculation model is the same,and the perfect sample selection method should select the inner point as the sample point as much as possible,and try to avoid the participation of the outer point to cause the result of the calculation model to be polluted.A feasible method is to assign an appropriate weight for each sample point according to the Euclidean distance.The higher the weight assigned,the greater the chance that this point is an interior point of the model.On the basis of assigning weights,with appropriate sample selection strategies,making the probability of inner points in the sample points used to calculate the model higher,which accelerates the process of RANSAC algorithm to some extent.(2)In the iteration of RANSAC,this paper proposes a uniform distribution sampling method.The entire RANSAC iteration is a process of trying new sample point combinations.The model calculated by the over clustered sample point cluster reflects the model properties of the sample point cluster and its neighborhood more.In response to this situation,this paper proposes scattered sampling in RANSAC to make the distribution of sampling points more dispersed and prevent excessive clustering of sample points.(2)Propose partition detection of feature points.The distribution of image feature points has a great influence on image stitching.It is found that the number of feature points of many images has a polarization trend.That is when the feature points of a certain area are piled,the redundant feature points of this area will be used as sample points for subsequent calculations,which will highlight the characteristics of the local area that is not good for the entire image.On the contrary,there will be very few or no feature points in a region.If this happens in the overlapping area between images,there will be no enough feature points making image stitching impossible.The optimal distribution of image feature points should tend to be uniform,and sufficient feature points can be provided in the overlapping area of the image.Therefore,this paper proposes to divide the whole image into several blocks of equal size,and then generate enough feature points in each sub partition image.(3)Set different feature detection modes for images with different feature distributions.The image stitching process in this article is designed to differentiate from the general stitching methods of other image stitching software.After in-depth comparison of several mainstream feature point detection methods(including surf,fast,akaze,sift and brisk),the above algorithms are implemented by code and then compared.Through the analysis of the experimental results,we can know the advantages and disadvantages of each algorithm,and we can find the suitable methods for different types of images.It will provide more feature detection modes in the later image stitching process.Finally,the above image stitching process is realized through python,in which the number of iterations and running time of the improved RANSAC algorithm and the original RANSAC algorithm are compared.At the same time,it also added an improved algorithm as a comparison group.It proves that the improved algorithm proposed in this paper has a significant improvement in solving the homography matrix model compared with the original algorithm and other improved algorithms.