Paper Title:
A New Method for Feature Point Matching: Inner and Exterior Product
  Abstract

In this paper, a novel feature descriptor called gradient correlation descriptor (GCD) is proposed. The GCD descriptor uses the gradient correlation measure defined by the inner and exterior product to characterize the gradient distributions in neighborhoods of feature points, and it has the following advantages: Its construction is very simple because of only the inner and exterior product operations are used; Its distinctive performance is better than the region-based SIFT descriptors since the gradient correlation measure can effectively characterize the gradient distributions in neighborhoods of feature points; In the gradient correlation measure the use of gradient mean makes it is not sensitive to the estimate precision of main orientation of feature point, and thus can provide a better stabilization to image rotation; The gradient correlation measure makes it also has very good adaptability to image affine transform, image blur, JPEG compression as well as illumination change.

  Info
Periodical
Edited by
Zhixiang Hou
Pages
79-83
DOI
10.4028/www.scientific.net/AMM.48-49.79
Citation
X. G. Wang, L. J. Lin, H. Y. Cheng, "A New Method for Feature Point Matching: Inner and Exterior Product", Applied Mechanics and Materials, Vols. 48-49, pp. 79-83, 2011
Online since
February 2011
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Price
$32.00
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