Research On Image Matching Method Based On Improved SURF Algorithm

Image matching as a large branch of image processing technology,in recent years in the field of image processing occupy an important position.Many of the computer visual aspects of the study are assiumed to match the problem has been resolved after the work.For image matching,the current image matching algorithm includes two categories:one is based on the gray level matching,one is based on the feature point of the match.In this paper,the research on image matching is carried out according to the feature point matching.Some classical feature matching algorithms are studied in detail,and then the SURF algorithm is improved.First of all,this paper summarizes the research and development of image matching at home and abroad,and analyzes the current research situation and common research methods at home and abroad under the popular feature matching algorithm.Secondly,the author introduces the theory of some classical matching algorithms based on feature points,including the extraction of feature points,the establishment and matching of feature descriptors.At the same time,it depends on the OpenCV development of computer vision open source library,compares the performance of various matching algorithms through concrete experiments,analyzes the experimental data and evaluates the experimental results.Finally,based on the OpenCV development of the open source visual library,the algorithm is combined with the SURF algorithm to establish the descriptor,and the matching precision is improved by using the LBPH evolution algorithm in the matching method.In the improved SURF algorithm,it is found that the improved algorithm has a significant improvement in matching accuracy,although the matching efficiency is improved.