{"title":"一种基于有效子图提取和特征点双向匹配的异源图像高精度配准算法","authors":"Xiujie Qu, Yue Sun, Yue Gu, Shuang Yu, Liwen Gao","doi":"10.1109/FSKD.2016.7603457","DOIUrl":null,"url":null,"abstract":"Aiming at solving the problem of low matching accuracy caused by different imaging mechanism of heterologous image, we propose a novel image registration algorithm based on effective sub-image extraction and bidirectional matching for surf feature points. The algorithm adopts a coarse-to-fine matching strategy. Firstly, we transform the edge image into frequency domain through fast Fourier transform, and roughly estimate transform parameters using the cross power spectrum; secondly, we divide the images after rough matching into several sub-graphs, from which we will pick out the effective sub-graph in terms of normalized mutual information, then we match bidirectionally the feature points of effective sub-graph pair according to time domain features, thus obtaining accurate transformation parameters, completing the fine matching. Experimental results of heterologous images in different scenarios show that, the proposed algorithm effectively improves the registration accuracy which is up to sub pixel level.","PeriodicalId":373155,"journal":{"name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A high-precision registration algorithm for heterologous image based on effective sub-graph extraction and feature points bidirectional matching\",\"authors\":\"Xiujie Qu, Yue Sun, Yue Gu, Shuang Yu, Liwen Gao\",\"doi\":\"10.1109/FSKD.2016.7603457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at solving the problem of low matching accuracy caused by different imaging mechanism of heterologous image, we propose a novel image registration algorithm based on effective sub-image extraction and bidirectional matching for surf feature points. The algorithm adopts a coarse-to-fine matching strategy. Firstly, we transform the edge image into frequency domain through fast Fourier transform, and roughly estimate transform parameters using the cross power spectrum; secondly, we divide the images after rough matching into several sub-graphs, from which we will pick out the effective sub-graph in terms of normalized mutual information, then we match bidirectionally the feature points of effective sub-graph pair according to time domain features, thus obtaining accurate transformation parameters, completing the fine matching. Experimental results of heterologous images in different scenarios show that, the proposed algorithm effectively improves the registration accuracy which is up to sub pixel level.\",\"PeriodicalId\":373155,\"journal\":{\"name\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2016.7603457\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2016.7603457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A high-precision registration algorithm for heterologous image based on effective sub-graph extraction and feature points bidirectional matching
Aiming at solving the problem of low matching accuracy caused by different imaging mechanism of heterologous image, we propose a novel image registration algorithm based on effective sub-image extraction and bidirectional matching for surf feature points. The algorithm adopts a coarse-to-fine matching strategy. Firstly, we transform the edge image into frequency domain through fast Fourier transform, and roughly estimate transform parameters using the cross power spectrum; secondly, we divide the images after rough matching into several sub-graphs, from which we will pick out the effective sub-graph in terms of normalized mutual information, then we match bidirectionally the feature points of effective sub-graph pair according to time domain features, thus obtaining accurate transformation parameters, completing the fine matching. Experimental results of heterologous images in different scenarios show that, the proposed algorithm effectively improves the registration accuracy which is up to sub pixel level.