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Proceedings Paper

Automated corresponding point candidate selection for image registration using wavelet transformation, neural network with rotation invariant inputs, and context information about neighboring candidates
Author(s): Hiroshi Okumura; Masashi Suezaki; Hideki Sueyasu; Kohei Arai
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Paper Abstract

An automated method that can select corresponding point candidates is developed. This method has the following three features: 1) employment of the RIN-net for corresponding point candidate selection; 2) employment of multi resolution analysis with Haar wavelet transformation for improvement of selection accuracy and noise tolerance; 3) employment of context information about corresponding point candidates for screening of selected candidates. Here, the 'RIN-net' means the back-propagation trained feed-forward 3-layer artificial neural network that feeds rotation invariants as input data. In our system, pseudo Zernike moments are employed as the rotation invariants. The RIN-net has N x N pixels field of view (FOV). Some experiments are conducted to evaluate corresponding point candidate selection capability of the proposed method by using various kinds of remotely sensed images. The experimental results show the proposed method achieves fewer training patterns, less training time, and higher selection accuracy than conventional method.

Paper Details

Date Published: 13 March 2003
PDF: 10 pages
Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); doi: 10.1117/12.463147
Show Author Affiliations
Hiroshi Okumura, Saga Univ. (Japan)
Masashi Suezaki, Saga Univ. (Japan)
Hideki Sueyasu, Saga Univ. (Japan)
Kohei Arai, Saga Univ. (Japan)

Published in SPIE Proceedings Vol. 4885:
Image and Signal Processing for Remote Sensing VIII
Sebastiano B. Serpico, Editor(s)

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