[IPOL announce] new article: Local Region Expansion: a Method for Analyzing and Refining Image Matches

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2017/154/

Erez Farhan, Elad Meir, and Rami Hagege,
Local Region Expansion: a Method for Analyzing and Refining Image Matches,
Image Processing On Line, 7 (2017), pp. 386–398.
https://doi.org/10.5201/ipol.2017.154

Abstract
We present a novel method for locating large amounts of local matches 
between images, with highly accurate localization. Point matching is one 
of the most fundamental tasks in computer vision, extensively used in 
applications such as object detection, object tracking and structure 
from motion. The major challenge in point matching is to preserve large 
numbers of accurate matches between corresponding scene locations under 
different geometric and radiometric conditions, while keeping the number 
of false positives low. Recent publications have shown that applying an 
affine transformation model on local regions is a particularly suitable 
approach for point matching. Yet, affine invariant methods are not used 
extensively for two reasons: first, because these methods are 
computationally demanding; and second because the derived affine 
estimations have limited accuracy. In this work, we propose a novel 
method of region expansion that enhances region matches detected by any 
state-of-the-art method. The method is based on accurate estimation of 
affine transformations, which are used to predict matching locations 
beyond initially detected matches. We use the improved estimations of 
affine transformations to locally verify tentative matches in an 
efficient way. We systematically reject false matches, while improving 
the localization of correct matches that are usually rejected by 
state-of-the-art methods.