學術產出-Proceedings

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 Integrating SIFT and Harris corner detector for definite keypoint extraction for image matching
作者 Chen, Chengyi;Chio, Shih Hong
邱式鴻
貢獻者 地政學系
關鍵詞 Feature point extraction; Harris corner detector; Keypoints; Scale and rotation; Scale invariant feature transforms; SIFT; Algorithms; Feature extraction; Photogrammetry; Remote sensing; Image matching
日期 2011
上傳時間 8-Oct-2015 17:36:31 (UTC+8)
摘要 Feature point extraction automatically instead of manually is important and can improve the efficiency for photogrammetric tasks. Scale invariant feature transform (SIFT) is an important algorithm developed by Lowe (2004) to extract keypoints with invariance on scale and rotation for image matching and registration. Not all these kinds of keypoints do correspond to the definite position of object points in real world, therefore not all of them are suitable for precise localization in photogrammetric task. The definite keypoints can be used for image matching and precise location of object points. For photogrammetric applications, Harris corner detector is often employed to detect definite and precise keypoints, but those kinds of keypoints do not have the characteristics of invariance on scale and rotation. Therefore, this study will integrate SIFT and Harris corner detector to extract the precise and definite keypoints for image matching in order to determine the precise location of object points. Meanwhile, the efficiency of image matching from the extracted keypoints will be investigated in this study.
關聯 32nd Asian Conference on Remote Sensing 2011, ACRS 2011
資料類型 conference
dc.contributor 地政學系-
dc.creator (作者) Chen, Chengyi;Chio, Shih Hong-
dc.creator (作者) 邱式鴻-
dc.date (日期) 2011-
dc.date.accessioned 8-Oct-2015 17:36:31 (UTC+8)-
dc.date.available 8-Oct-2015 17:36:31 (UTC+8)-
dc.date.issued (上傳時間) 8-Oct-2015 17:36:31 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/78886-
dc.description.abstract (摘要) Feature point extraction automatically instead of manually is important and can improve the efficiency for photogrammetric tasks. Scale invariant feature transform (SIFT) is an important algorithm developed by Lowe (2004) to extract keypoints with invariance on scale and rotation for image matching and registration. Not all these kinds of keypoints do correspond to the definite position of object points in real world, therefore not all of them are suitable for precise localization in photogrammetric task. The definite keypoints can be used for image matching and precise location of object points. For photogrammetric applications, Harris corner detector is often employed to detect definite and precise keypoints, but those kinds of keypoints do not have the characteristics of invariance on scale and rotation. Therefore, this study will integrate SIFT and Harris corner detector to extract the precise and definite keypoints for image matching in order to determine the precise location of object points. Meanwhile, the efficiency of image matching from the extracted keypoints will be investigated in this study.-
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) 32nd Asian Conference on Remote Sensing 2011, ACRS 2011-
dc.subject (關鍵詞) Feature point extraction; Harris corner detector; Keypoints; Scale and rotation; Scale invariant feature transforms; SIFT; Algorithms; Feature extraction; Photogrammetry; Remote sensing; Image matching-
dc.title (題名) Integrating SIFT and Harris corner detector for definite keypoint extraction for image matching-
dc.type (資料類型) conferenceen