There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 60 | - |
dc.citation.startPage | 53 | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 106 | - |
dc.contributor.author | Bailo, Oleksandr | - |
dc.contributor.author | Rameau, Francois | - |
dc.contributor.author | Joo, Kyungdon | - |
dc.contributor.author | Park, Jinsun | - |
dc.contributor.author | Bogdan, Oleksandr | - |
dc.contributor.author | Kweon, In So | - |
dc.date.accessioned | 2023-12-21T20:48:04Z | - |
dc.date.available | 2023-12-21T20:48:04Z | - |
dc.date.created | 2020-11-03 | - |
dc.date.issued | 2018-04 | - |
dc.description.abstract | Keypoint detection usually results in a large number of keypoints which are mostly clustered, redundant, and noisy. These keypoints often require special processing like Adaptive Non-Maximal Suppression (ANMS) to retain the most relevant ones. In this paper, we present three new efficient ANMS approaches which ensure a fast and homogeneous repartition of the keypoints in the image. For this purpose, a square approximation of the search range to suppress irrelevant points is proposed to reduce the computational complexity of the ANMS. To further speed up the proposed approaches, we also introduce a novel strategy to initialize the search range based on image dimension which leads to a faster convergence. An exhaustive survey and comparisons with already existing methods are provided to highlight the effectiveness and scalability of our methods and the initialization strategy. (c) 2018 Elsevier B.V. All rights reserved. | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.106, pp.53 - 60 | - |
dc.identifier.doi | 10.1016/j.patrec.2018.02.020 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.scopusid | 2-s2.0-85042693608 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/48701 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S016786551830062X | - |
dc.identifier.wosid | 000429325500009 | - |
dc.language | 영어 | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.title | Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Adaptive non-maximal suppression | - |
dc.subject.keywordAuthor | Point detection | - |
dc.subject.keywordAuthor | SLAM | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.