Selective Image Diffusion for Oriented Pattern Extraction
Abstract
Anisotropic regularization PDE's (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, a selective diffusion approach based on the framework of Extreme Physical Information theory is presented. It is shown that this particular framework leads to a particular regularization PDE which makes it possible integration of prior knowledge within diffusion scheme. As a proof a feasibility, results of oriented pattern extractions are presented on ad hoc images. This approach may find applicability in vision in robotics.