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題名 Improved sift algorithm to match points in the texture region image
作者 Chen, Chen C.-Y.;Chio, Shih-Hong
邱式鴻
貢獻者 地政學系
關鍵詞 Feature point extraction; Harris corner detector; Matching problems; Scale and rotation; Scale invariant feature transforms; SIFT; SIFT algorithms; SIFT descriptors; Algorithms; Feature extraction; Image texture; Remote sensing; Textures; Image matching
日期 2012
上傳時間 10-Apr-2015 16:34:31 (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. SIFT uses descriptor to descript a keypoint and employs these descriptors as keypoint`s fingerprint in matching. But when it comes to texture region images, SIFT will produce a large number of similar descriptors and these similar descriptors will generatewrong or failedmatching results. In this study, a new algorithm based on SIFT is designed to deal with the matching problem in texture region images. The new algorithm uses the concept of Harris Corner Detector and the entropy in order to make SIFT descriptors more independence. Thus even keypoints are in texture region image, the descriptor of every keypoints is still unique enough to other keypoints. From the tests, this developed new algorithm can make the successful rate of matching up to about 80% in texture region images. The performance of developed algorithm is better than SIFT algorithm.
關聯 33rd Asian Conference on Remote Sensing 2012, ACRS 2012
資料類型 conference
dc.contributor 地政學系
dc.creator (作者) Chen, Chen C.-Y.;Chio, Shih-Hong
dc.creator (作者) 邱式鴻zh_TW
dc.date (日期) 2012
dc.date.accessioned 10-Apr-2015 16:34:31 (UTC+8)-
dc.date.available 10-Apr-2015 16:34:31 (UTC+8)-
dc.date.issued (上傳時間) 10-Apr-2015 16:34:31 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74469-
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. SIFT uses descriptor to descript a keypoint and employs these descriptors as keypoint`s fingerprint in matching. But when it comes to texture region images, SIFT will produce a large number of similar descriptors and these similar descriptors will generatewrong or failedmatching results. In this study, a new algorithm based on SIFT is designed to deal with the matching problem in texture region images. The new algorithm uses the concept of Harris Corner Detector and the entropy in order to make SIFT descriptors more independence. Thus even keypoints are in texture region image, the descriptor of every keypoints is still unique enough to other keypoints. From the tests, this developed new algorithm can make the successful rate of matching up to about 80% in texture region images. The performance of developed algorithm is better than SIFT algorithm.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) 33rd Asian Conference on Remote Sensing 2012, ACRS 2012
dc.subject (關鍵詞) Feature point extraction; Harris corner detector; Matching problems; Scale and rotation; Scale invariant feature transforms; SIFT; SIFT algorithms; SIFT descriptors; Algorithms; Feature extraction; Image texture; Remote sensing; Textures; Image matching
dc.title (題名) Improved sift algorithm to match points in the texture region image
dc.type (資料類型) conferenceen