{"title":"基于局部不变区域识别的立体匹配","authors":"Qian Hu, Zhengqiu Yang","doi":"10.1109/ISCSCT.2008.69","DOIUrl":null,"url":null,"abstract":"Among the solutions for stereo matching which is regarded as one fundamental topic in computer vision, methods based on regions can overcome the drawbacks of methods using points and lines as the features since regions can more exactly reflect the inherent properties of images processed. In this paper, we propose an efficient method to extract the local invariant region, in which the affine moment invariant is also combined. Experiments prove our method is effective while easier to implement.","PeriodicalId":228533,"journal":{"name":"2008 International Symposium on Computer Science and Computational Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Stereo Matching Based on Local Invariant Region Identification\",\"authors\":\"Qian Hu, Zhengqiu Yang\",\"doi\":\"10.1109/ISCSCT.2008.69\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Among the solutions for stereo matching which is regarded as one fundamental topic in computer vision, methods based on regions can overcome the drawbacks of methods using points and lines as the features since regions can more exactly reflect the inherent properties of images processed. In this paper, we propose an efficient method to extract the local invariant region, in which the affine moment invariant is also combined. Experiments prove our method is effective while easier to implement.\",\"PeriodicalId\":228533,\"journal\":{\"name\":\"2008 International Symposium on Computer Science and Computational Technology\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Symposium on Computer Science and Computational Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCSCT.2008.69\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on Computer Science and Computational Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCSCT.2008.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stereo Matching Based on Local Invariant Region Identification
Among the solutions for stereo matching which is regarded as one fundamental topic in computer vision, methods based on regions can overcome the drawbacks of methods using points and lines as the features since regions can more exactly reflect the inherent properties of images processed. In this paper, we propose an efficient method to extract the local invariant region, in which the affine moment invariant is also combined. Experiments prove our method is effective while easier to implement.