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Open AccessArticle

An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses

1
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Peñalolén, Santiago 7941169, Chile
2
Departamento de Ciencia de la Computación, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
3
Facultad de Ingeniería, Universidad Panamericana, Aguascalientes, Aguascalientes 20290, Mexico
4
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
Electronics 2021, 10(3), 246; https://doi.org/10.3390/electronics10030246
Received: 30 December 2020 / Revised: 15 January 2021 / Accepted: 18 January 2021 / Published: 22 January 2021
(This article belongs to the Special Issue Applications of Computer Vision)
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. View Full-Text
Keywords: computer vision; correspondence problem; fundamental matrix; multiple view geometry; point matching; trifocal tensor computer vision; correspondence problem; fundamental matrix; multiple view geometry; point matching; trifocal tensor
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MDPI and ACS Style

Carrasco, M.; Mery, D.; Concha, A.; Velázquez, R.; De Fazio, R.; Visconti, P. An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses. Electronics 2021, 10, 246. https://doi.org/10.3390/electronics10030246

AMA Style

Carrasco M, Mery D, Concha A, Velázquez R, De Fazio R, Visconti P. An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses. Electronics. 2021; 10(3):246. https://doi.org/10.3390/electronics10030246

Chicago/Turabian Style

Carrasco, Miguel; Mery, Domingo; Concha, Andrés; Velázquez, Ramiro; De Fazio, Roberto; Visconti, Paolo. 2021. "An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses" Electronics 10, no. 3: 246. https://doi.org/10.3390/electronics10030246

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