Local Region Expansion: a Method for Analyzing and Refining Image Matches
Erez Farhan, Elad Meir, Rami Hagege
→ BibTeX
    title   = {{Local Region Expansion: a Method for Analyzing and Refining Image Matches}},
    author  = {Farhan, Erez and Meir, Elad and Hagege, Rami},
    journal = {{Image Processing On Line}},
    volume  = {7},
    pages   = {386--398},
    year    = {2017},
    doi     = {10.5201/ipol.2017.154},
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.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

Communicated by Luis Álvarez, Martín Rais
Demo edited by Erez Farhan, Elad Meir

This IPOL article is related to a companion publication in the SIAM Journal on Imaging Sciences:
Erez Farhan, Rami Hagege, “Geometric Expansion for Local Feature Analysis and Matching” SIAM Journal on Imaging Sciences, vol. 8, no. 4, pp. 2771-2813, 2015.


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.