Authors:
S. Metari
1
and
François Deschênes
2
Affiliations:
1
Université de Sherbrooke, Canada
;
2
Université du Québec en Outaouais, Canada
Keyword(s):
Radiometric invariants, Combined invariants, Moments, Mellin transform, Point Spread Function.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
In this paper we introduce two new classes of radiometric and combined radiometric-geometric invariant descriptors. The first class includes two types of radiometric descriptors. The first type is based on the Mellin transform and the second one is based on central moments. Both descriptors are invariant to contrast changes and to convolution with any kernel having a symmetric form with respect to the diagonals. The second class contains two subclasses of combined descriptors. The first subclass includes central-moment based descriptors invariant simultaneously to translations, to uniform and anisotropic scaling, to stretching, to contrast changes and to convolution. The second subclass includes central-complex-moment based descriptors invariant simultaneously to similarity transformation and to contrast changes. We apply those invariants to the matching of geometrically transformed and/or blurred images.