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Proceedings Paper

Matching image feature structures using shoulder analysis method
Author(s): Boris Kovalerchuk; William Q. Sumner; Mark Curtiss; Michael Kovalerchuk; Richard Chase
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Paper Abstract

The problems of imagery registration, conflation, fusion and search require sophisticated and robust methods. An algebraic approach is a promising new option for developing such methods. It is based on algebraic analysis of features represented as polylines. The problem of choosing points when attempting to prepare a linear feature for comparison with other linear features is a significant challenge when orientation and scale is unknown. Previously we developed an invariant method known as Binary Structural Division (BSD). It is shown to be effective in comparing feature structure for specific cases. In cases where a bias of structure variability exists however, this method performs less well. A new method of Shoulder Analysis (SA) has been found which enhances point selection, and improves the BSD method. This paper describes the use of shoulder values, which compares the actual distance traveled along a feature to the linear distance from the start to finish of the segment. We show that shoulder values can be utilized within the BSD method, and lead to improved point selection in many cases. This improvement allows images of unknown scale and orientation to be correlated more effectively.

Paper Details

Date Published: 12 August 2004
PDF: 12 pages
Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); doi: 10.1117/12.542564
Show Author Affiliations
Boris Kovalerchuk, Central Washington Univ. (United States)
William Q. Sumner, Central Washington Univ. (United States)
Mark Curtiss, Central Washington Univ. (United States)
Michael Kovalerchuk, Central Washington Univ. (United States)
Richard Chase, Central Washington Univ. (United States)


Published in SPIE Proceedings Vol. 5425:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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