Authors:
A. Histace
1
;
V. Courboulay
2
and
M. Ménard
2
Affiliations:
1
ETIS UMR CNRS 8051, ENSEA-UCP, France
;
2
L3i, University of La Rochelle, France
Keyword(s):
Image Diffusion, Extreme Physical Information, Oriented Pattern Extraction, Selectivity.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
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.