An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses

Miguel Carrasco; Domingo Mery; Andrés Concha; Velázquez, Ramiro; Roberto De Fazio; Paolo Visconti

doi:10.3390/electronics10030246

An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses

Journal

Electronics

ISSN

2079-9292

Date Issued

2021

Author(s)

Miguel Carrasco

Domingo Mery

Andrés Concha

Velázquez, Ramiro

Facultad de Ingeniería - CampAGS

Roberto De Fazio

Paolo Visconti

Type

text::journal::journal article

DOI

10.3390/electronics10030246

URL

https://scripta.up.edu.mx/handle/20.500.12552/3192

Abstract

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.