{"title":"仿射不变局部检测器与描述子的比较","authors":"K. Mikolajczyk, C. Schmid","doi":"10.5281/ZENODO.38605","DOIUrl":null,"url":null,"abstract":"In this paper we summarize recent progress on local photometric invariants. The photometric invariants can be used to find correspondences in the presence of significant viewpoint changes. We evaluate the performance of region detectors and descriptors. We compare several methods for detecting affine regions [4, 9, 11, 18, 17]. We evaluate the repeatability of the detected regions, the accuracy of the detectors and the invariance to geometric as well as photometric image transformations. Furthermore, we compare several local descriptors [3, 5, 8, 14, 19]. The local descriptors are evaluated in terms of two properties: robustness and distinctiveness. The evaluation is carried out for different image transformations and scene types. We observe that the ranking of the detectors and descriptors remains the same regardless the scene type or image transformation.","PeriodicalId":347658,"journal":{"name":"2004 12th European Signal Processing Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"56","resultStr":"{\"title\":\"Comparison of affine-invariant local detectors and descriptors\",\"authors\":\"K. Mikolajczyk, C. Schmid\",\"doi\":\"10.5281/ZENODO.38605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we summarize recent progress on local photometric invariants. The photometric invariants can be used to find correspondences in the presence of significant viewpoint changes. We evaluate the performance of region detectors and descriptors. We compare several methods for detecting affine regions [4, 9, 11, 18, 17]. We evaluate the repeatability of the detected regions, the accuracy of the detectors and the invariance to geometric as well as photometric image transformations. Furthermore, we compare several local descriptors [3, 5, 8, 14, 19]. The local descriptors are evaluated in terms of two properties: robustness and distinctiveness. The evaluation is carried out for different image transformations and scene types. We observe that the ranking of the detectors and descriptors remains the same regardless the scene type or image transformation.\",\"PeriodicalId\":347658,\"journal\":{\"name\":\"2004 12th European Signal Processing Conference\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"56\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 12th European Signal Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.38605\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 12th European Signal Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.38605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of affine-invariant local detectors and descriptors
In this paper we summarize recent progress on local photometric invariants. The photometric invariants can be used to find correspondences in the presence of significant viewpoint changes. We evaluate the performance of region detectors and descriptors. We compare several methods for detecting affine regions [4, 9, 11, 18, 17]. We evaluate the repeatability of the detected regions, the accuracy of the detectors and the invariance to geometric as well as photometric image transformations. Furthermore, we compare several local descriptors [3, 5, 8, 14, 19]. The local descriptors are evaluated in terms of two properties: robustness and distinctiveness. The evaluation is carried out for different image transformations and scene types. We observe that the ranking of the detectors and descriptors remains the same regardless the scene type or image transformation.