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.
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