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Auteur Fengman Jia |
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vol VIII-4/W2-2021 - [Actes] ISPRS TC IV 16th 3D GeoInfo Conference 2021 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol VIII-4/W2-2021 [07/10/2021]) / Linh Truong-HongA robust image matching method based on optimized BaySAC / Zhizhong Kang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)
[article]
Titre : A robust image matching method based on optimized BaySAC Type de document : Article/Communication Auteurs : Zhizhong Kang, Auteur ; Fengman Jia, Auteur ; Liqiang Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 1041 - 1052 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] classification bayesienne
[Termes IGN] couple stéréoscopique
[Termes IGN] méthode robuste
[Termes IGN] Ransac (algorithme)
[Termes IGN] SIFT (algorithme)Résumé : (Auteur)This paper proposes a robust image-matching method, which integrates SIFT with the optimized Bayes SAmpling Consensus (BaySAC). As the point correspondences are likely contaminated by outliers, we present a novel robust estimation method involving an efficient RaySAC for eliminating falsely accepted correspondences. The key points of the proposed hypothesis testing algorithm are determining and updating the prior probabilities of pseudo-correspondences. First, we propose a strategy for prior probability determination in terms of the statistical characteristics of a deterministic mathematical model for hypothesis testing. Moreover, the inlier probability updating is simplified based on a memorable form of Bayes' Theorem. The proposed approach is validated on a variety of image pairs. The results indicate that when compared with the performance of RANdom SAmpling Consensus (IIANSAC) and the original BaySAC, the proposed optimized BaySAC consumes less computation and obtains higher matching accuracy when the hypothesis set is contaminated with more outliers. Numéro de notice : A2014-616 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.11.1041 En ligne : https://doi.org/10.14358/PERS.80.11.1041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74922
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 11 (November 2014) . - pp 1041 - 1052[article]