Name
Abstract | ||
|---|---|---|
In this paper, a novel image registration method is proposed. In the proposed method, kernel independent component analysis (KICA) is applied to extract features from the image sets, and these features are input vectors of feedforward neural networks (FNN). Neural network outputs are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments are performed between KICA based method and other six feature extraction based method: principal component analysis, (PCA), independent component analysis (ICA), kernel principal component analysis (KPCA), the discrete cosine transform (DCT), Zernike moment and the complete isometric mapping (Isomap). The results show that the proposed method is much improved not only at accuracy but also remarkably at robust to noise. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.247371 | 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 |
Keywords | Field | DocType |
feedforward neural network,principal component analysis,independent component analysis,image registration,neural network,discrete cosine transform,feature extraction,kernel principal component analysis | Computer science,Discrete cosine transform,Kernel principal component analysis,Artificial intelligence,Computer vision,Pattern recognition,Feature extraction,Probabilistic neural network,Independent component analysis,Machine learning,Image registration,Principal component analysis,Isomap | Conference |
ISSN | Citations | PageRank |
2161-4393 | 8 | 0.59 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anbang Xu | 1 | 351 | 30.52 |
| Xin Jin | 2 | 8 | 0.59 |
| Ping Guo | 3 | 601 | 85.05 |
| Rongfang Bie | 4 | 547 | 68.23 |