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Proceedings Paper

Neural-net-based image matching
Author(s): Anna K. Jerebko; Nikita E. Barabanov; Vadim R. Luciv; Nigel M. Allinson
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Paper Abstract

The paper describes a neural-based method for matching spatially distorted image sets. The matching of partially overlapping images is important in many applications-- integrating information from images formed from different spectral ranges, detecting changes in a scene and identifying objects of differing orientations and sizes. Our approach consists of extracting contour features from both images, describing the contour curves as sets of line segments, comparing these sets, determining the corresponding curves and their common reference points, calculating the image-to-image co-ordinate transformation parameters on the basis of the most successful variant of the derived curve relationships. The main steps are performed by custom neural networks. The algorithms describe in this paper have been successfully tested on a large set of images of the same terrain taken in different spectral ranges, at different seasons and rotated by various angles. In general, this experimental verification indicates that the proposed method for image fusion allows the robust detection of similar objects in noisy, distorted scenes where traditional approaches often fail.

Paper Details

Date Published: 14 April 2000
PDF: 10 pages
Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); doi: 10.1117/12.382906
Show Author Affiliations
Anna K. Jerebko, Univ. of Manchester Institute of Science and Technology (United States)
Nikita E. Barabanov, St. Petersburg Electrical Univ. (Russia)
Vadim R. Luciv, S.I. Vavilov State Optical Institute (Russia)
Nigel M. Allinson, Univ. of Manchester Institute of Science and Technology (United Kingdom)

Published in SPIE Proceedings Vol. 3962:
Applications of Artificial Neural Networks in Image Processing V
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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