Automated corresponding point candidate selection for image registration using wavelet transformation, neural network with rotation invariant inputs, and context information about neighboring candidates

Hiroshi Okumura; Masashi Suezaki; Hideki Sueyasu; Kohei Arai

doi:10.1117/12.463147

13 March 2003 Automated corresponding point candidate selection for image registration using wavelet transformation neurla network with rotation invariant inputs and context information about neighboring candidates

Hiroshi Okumura, Masashi Suezaki, Hideki Sueyasu, Kohei Arai

Author Affiliations +

Proceedings Volume 4885, Image and Signal Processing for Remote Sensing VIII; (2003) https://doi.org/10.1117/12.463147
Event: International Symposium on Remote Sensing, 2002, Crete, Greece

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.

Citation Download Citation

Hiroshi Okumura, Masashi Suezaki, Hideki Sueyasu, and Kohei Arai "Automated corresponding point candidate selection for image registration using wavelet transformation neurla network with rotation invariant inputs and context information about neighboring candidates", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); https://doi.org/10.1117/12.463147

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