Multiscale Feature Extractors for Stereo Matching Cost Computation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Kyung-Rae | - |
dc.contributor.author | Koh, Yeong Jun | - |
dc.contributor.author | Kim, Chang-Su | - |
dc.date.accessioned | 2021-09-02T21:15:34Z | - |
dc.date.available | 2021-09-02T21:15:34Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/80950 | - |
dc.description.abstract | We propose four efficient feature extractors based on convolutional neural networks for stereo matching cost computation. Two of them generate multiscale features with diverse receptivefield sizes. These multiscale features are used to compute the corresponding multiscale matching costs. We then determine an optimal cost by combining the multiscale costs using edge information. On the other hand, the other two feature extractors produce uni-scale features by combining multiscale features directly through fully connected layers. Finally, after obtaining matching costs using one of the four extractors, we determine optimal disparities based on the cross-based cost aggregation and the semiglobal matching. Extensive experiments on the Middlebury stereo data sets demonstrate the effectiveness and efficiency of the proposed algorithm. Specifically, the proposed algorithm provides competitive matching performance with the state of the arts, while demanding lower computational complexity. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | BELIEF PROPAGATION | - |
dc.subject | EFFICIENT STEREO | - |
dc.title | Multiscale Feature Extractors for Stereo Matching Cost Computation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Chang-Su | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2838442 | - |
dc.identifier.scopusid | 2-s2.0-85047227341 | - |
dc.identifier.wosid | 000435512600001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.6, pp.27971 - 27983 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 6 | - |
dc.citation.startPage | 27971 | - |
dc.citation.endPage | 27983 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | BELIEF PROPAGATION | - |
dc.subject.keywordPlus | EFFICIENT STEREO | - |
dc.subject.keywordAuthor | Stereo matching | - |
dc.subject.keywordAuthor | matching cost computation | - |
dc.subject.keywordAuthor | multiscale feature extraction | - |
dc.subject.keywordAuthor | convolutional neural networks | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.