Abstract: Neural network-based image registration using global image features is relatively a new research subject and the schemes devised so far use a feedforward neural network to find the geometrical transformation parameters. In this work, we propose to use a radial basis function neural network instead of feedforward neural network to overcome lengthy pre-registration training stage. This modification has been tested on a typical neural network-based registration method using discrete cosine transformation features in the presence of noise. The proposed scheme does not only speed up the training stage enormously, but also increases the accuracy and robustness against additive white noise owing to the better generalization ability of the radial basis function neural networks.
Sarnel, H., & Senol, Y. (2008). Accurate affine image registration using radial basis neural networks. The International Conference on Electrical Engineering, 6(6th International Conference on Electrical Engineering ICEENG 2008), 1-6. doi: 10.21608/iceeng.2008.34357
MLA
Haldun Sarnel; Yavuz Senol. "Accurate affine image registration using radial basis neural networks", The International Conference on Electrical Engineering, 6, 6th International Conference on Electrical Engineering ICEENG 2008, 2008, 1-6. doi: 10.21608/iceeng.2008.34357
HARVARD
Sarnel, H., Senol, Y. (2008). 'Accurate affine image registration using radial basis neural networks', The International Conference on Electrical Engineering, 6(6th International Conference on Electrical Engineering ICEENG 2008), pp. 1-6. doi: 10.21608/iceeng.2008.34357
VANCOUVER
Sarnel, H., Senol, Y. Accurate affine image registration using radial basis neural networks. The International Conference on Electrical Engineering, 2008; 6(6th International Conference on Electrical Engineering ICEENG 2008): 1-6. doi: 10.21608/iceeng.2008.34357
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