An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses
| Autor: | Andres Concha, Domingo Mery, Paolo Visconti, Miguel Carrasco, Roberto de Fazio, Ramiro Velazquez |
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| Přispěvatelé: | Carrasco, Miguel, Mery, Domingo, Concha, André, Velázquez, Ramiro, DE FAZIO, Roberto, Visconti, Paolo |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science point matching lcsh:TK7800-8360 Correspondence analysis computer vision fundamental matrix Intersection Trifocal tensor Electrical and Electronic Engineering Invariant (mathematics) Fundamental matrix (computer vision) Correspondence problem Transformation geometry Computer vision correspondence problem fundamental matrix multiple view geometry point matching trifocal tensor multiple view geometry business.industry lcsh:Electronics Point set registration Pattern recognition trifocal tensor correspondence problem Hardware and Architecture Control and Systems Engineering Signal Processing Artificial intelligence business |
| Zdroj: | Electronics, Vol 10, Iss 246, p 246 (2021) Electronics Volume 10 Issue 3 |
| Popis: | Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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