A novel approach to accurate and robust image registration using feedforward neural networks is presented. Common registration schemes utilize some form of similarity measures in order to evaluate affine transformation parameters. In the proposed scheme, feedforward neural networks are employed as means of providing translation, rotation and scaling parameters with respect to reference and observed image sets. Discrete Cosine Transform (DCT) features are extracted as inputs to the network. Experimental results with several deformed and noisy images indicate that the proposed algorithm is both accurate and remarkably robust to diverse noisy conditions.

No abstract

No abstract