Neural-net-based image matching

Anna K. Jerebko; Nikita E. Barabanov; Vadim R. Luciv; Nigel M. Allinson

doi:10.1117/12.382906

14 April 2000 Neural-net-based image matching

Anna K. Jerebko, Nikita E. Barabanov, Vadim R. Luciv, Nigel M. Allinson

Proceedings Volume 3962, Applications of Artificial Neural Networks in Image Processing V; (2000) https://doi.org/10.1117/12.382906
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

Anna K. Jerebko, Nikita E. Barabanov, Vadim R. Luciv, and Nigel M. Allinson "Neural-net-based image matching", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); https://doi.org/10.1117/12.382906

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