Buscar

An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses

RL1, Publisher: Electronics, Link>


AUTHORS

Miguel Carrasco, Domingo Mery, Andres Concha, Ramiro Velazquez, Roberto De Fazio, Paolo Visconti


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.

0 visualizaciones

Entradas Recientes

Ver todo

RL2, Publisher: Journal of Machine Learning Research, Link> AUTHORS Jorge Pérez, Pablo Barceló, Javier Marinkovic ABSTRACT Alternatives to recurrent neural networks, in particular, architectures bas

RL2, Publisher: https://github.com/pdm-book/community Link> AUTHORS Marcelo Arenas, Pablo Barceló, Leonid Libkin, Wim Martens, Andreas Pieris ABSTRACT This is a release of parts 1, 2, and 4 of the