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A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration / Z. Liu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 2 (February 2012) . - pp 514 - 527 Titre : A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration Type de document : Article/Communication Auteurs : Z. Liu, Auteur ; J. An, Auteur ; Y. Jing, Auteur Année de publication : 2012 Article en page(s) : pp 514 - 527 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] appariement de graphes [Termes IGN] appariement de points [Termes IGN] image aérienne [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] programmation par contraintes [Termes IGN] superposition d'images [Termes IGN] valeur aberrante Résumé : (Auteur) Accurate point matching is a critical and challenging process in feature-based image registration. In this paper, a simple and robust feature point matching algorithm, called Restricted Spatial Order Constraints (RSOC), is proposed to remove outliers for registering aerial images with monotonous backgrounds, similar patterns, low overlapping areas, and large affine transformation. In RSOC, both local structure and global information are considered. Based on adjacent spatial order, an affine invariant descriptor is defined, and point matching is formulated as an optimization problem. A graph matching method is used to solve it and yields two matched graphs with a minimum global transformation error. In order to eliminate dubious matches, a filtering strategy is designed. The strategy integrates two-way spatial order constraints and two decision criteria restrictions, i.e., the stability and accuracy of transformation error. Twenty-nine pairs of optical and Synthetic Aperture Radar (SAR) aerial images are utilized to evaluate the performance. Compared with RANdom SAmple Consensus (RANSAC), Graph Transformation Matching (GTM), and Spatial Order Constraints (SOC), RSOC obtained the highest precision and stability. Numéro de notice : A2012-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2160645 Date de publication en ligne : 04/08/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2160645 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31494 [article]

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