Neural net based image matching

Jerebko, Anna K., Barabanov, Nikita E., Luciv, Vadim R. and Allinson, Nigel M. (2000) Neural net based image matching. In: Applications of Artificial Neural Networks in Image Processing V, 27-28 January 2000, San Jose, CA, USA.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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 described 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.

Keywords:Image analysis, Image quality, Neural networks, Contour extraction, Image matching, Feature extraction
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8583
Deposited On:19 Apr 2013 11:50

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