Authors:
Pier Paolo Campari
;
Matteo Matteucci
and
Davide Migliore
Affiliation:
Politecnico di Milano, Italy
Keyword(s):
Feature Extraction, Feature Description, Color Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
Geodesic invariant feature have been originally proposed to build a new local feature descriptor invariant not only to affine transformations, but also to general deformations. The aim of this paper is to investigate the possible improvements given by the use of color information in this kind of descriptor. We introduced color information both in geodesic feature construction and description. At feature construction level, we extended the fast marching algorithm to use color information; at description level, we tested several color spaces on real data and we devised the opponent color space as an useful integration to intensity information. The experiments used to validate our theory are based on publicly available data and show the improvement, in precision and recall, with respect to the original intensity based geodesic features. We also compared this kind of features, on affine and non affine transformation, with SIFT, steerable filters, moments invariants, spin images and GIH.