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Image matching using SIFT features and relaxation labeling technique—A constraint initializing method for dense stereo matching / Jyoti Joglekar in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 9 Tome 1 (September 2014) . - pp 5643 - 5652 Titre : Image matching using SIFT features and relaxation labeling technique—A constraint initializing method for dense stereo matching Type de document : Article/Communication Auteurs : Jyoti Joglekar, Auteur ; Shirish S. Gedam, Auteur ; B.K. Mohan, Auteur Année de publication : 2014 Article en page(s) : pp 5643 - 5652 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] appariement dense [Termes IGN] processus stochastique [Termes IGN] SIFT (algorithme) [Termes IGN] vision stéréoscopique Résumé : (Auteur) A probabilistic neural-network-based feature-matching algorithm for a stereo image pair is presented in this paper, which will be useful as a constraint initializing method for further dense matching technique. In this approach, scale-invariant feature transform (SIFT) features are used to detect interest points in a stereo image pair. The descriptor which is associated with each keypoint is based on the histogram of the gradient magnitude and direction of gradients. These descriptors are the preliminary input for the matching algorithm. Using disparity range computed by visual inspection, the search area can be restricted for a given stereo image pair. Reduced search area improves the computation speed. Initial probabilities of matches are assigned to the keypoints which are considered as probable matches from the selected search area by Bayesian reasoning. The probabilities of all such matches are improved iteratively using relaxation labeling technique. Neighboring probable matches are exploited to improve the probability of best match using consistency measures. Confidence measures considering the neighborhood, unicity, and symmetry are some validation techniques which are built into the technique presented here for finding accurate matches. The algorithm is found to be effective in matching SIFT features detected in a stereo image pair with greater accuracy, and these accurate correspondences can be used in finding the fundamental matrix which encodes the epipolar geometry between the given stereo image pair. This fundamental matrix can then be used as a constraint for finding inliers that are used in matching methods for deriving dense disparity map. Numéro de notice : A2014-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2291685 En ligne : https://doi.org/10.1109/TGRS.2013.2291685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73981 [article]

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