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.
Highlights
Introduction published maps and institutional affilPoint matching in two or more images is a very relevant and complex problem in computer vision
It finds applications in robot navigation, 3D object reconstruction, multiple-view tracking, and homography, among others. It basically consists of identifying a set of points across several images
This paper introduces introduces an an efficient efficient point-matching point-matching method method capable capable of of filtering filtering most most of the wrong correspondences
Summary
Point matching in two or more images is a very relevant and complex problem in computer vision. It finds applications in robot navigation, 3D object reconstruction, multiple-view tracking, and homography, among others. It basically consists of identifying a set of points across several images. Due to the different points of view from which the images were captured, the corresponding points might exhibit differences across the images Such differences are mainly due to the geometric transformations and photometric conditions determined by the continuous motion of both object and cameras [1].
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