Renormalization group approach to hierarchical image analysis

Image analysis is the most important step of image understanding. Changes in spatial image structure must be detected at different levels of detail and over different extents in order to extract image features at different scales from noisy images. The author formulates the problem as the minimization of an image energy that combines a smoothness term, a discrepancy term, and a nonlinear term. A hierarchical method for image analysis is established by applying the renormalization group procedure to the image energy. Simulation shows that the well-segmented images are obtained hierarchically, and that this approach is useful for coarse-to-fine matching in image analysis