[IPOL announce] new article: Matching of Weakly-Localized Features under Different Geometric Models

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2020/247/

Erez Farhan,
Matching of Weakly-Localized Features under Different Geometric Models,
Image Processing On Line, 10 (2020), pp. 1–23.
https://doi.org/10.5201/ipol.2020.247

Abstract
Matching corresponding local patches between images is a fundamental 
building block in many computer-vision algorithms, reducing the 
high-dimensional challenge of recovering geometric relations between 
images to a series of relatively simple and independent tasks. This 
approach is geometrically very flexible and has clear computational 
advantages over more convoluted global solutions. But it also has two 
major practical shortcomings: 1) Sparsity: the need to rely on 
high-quality repeatable features for matching drives current local 
methods to discard low-textured image locations and leave them 
unanalysed; 2) Reliability: the limited spatial context in which those 
methods work often does not contain enough information for achieving 
reliable matches. In this work, we target a major blind spot of local 
feature matching: ill-textured locations. We observe that while classic 
methods avoided using poorly localized features (e.g. edges) as matching 
candidates, due to their low reliability, these features contain highly 
valuable information for image registration. We show how, given the 
appropriate geometric context, reliable matches can be produced from 
these features, contributing to a better coverage of the scene. We 
present a statistically attractive framework for encoding the 
uncertainty that stems from using weakly localized matches into a 
coupled geometric estimation and match extraction process. We examine 
the practical application of the proposed framework to the problems of 
guided matching and affine region expansion and show significant 
improvement over preceding methods.