Authors:
JanKees van der Poel
1
;
Leonardo Vidal Batista
2
and
Carlos Wilson Dantas de Almeida
2
Affiliations:
1
Instituto de Educação Superior da Paráıba – IESP, Brazil
;
2
Universidade Federal da Paraíba, Brazil
Keyword(s):
Curvature Scale Space, CBIR, correlation coefficient, resampling, resizing, shape classification, full curvature values.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early Vision and Image Representation
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
;
Surface Geometry and Shape
Abstract:
This work presents a new multiscale, curvature-based shape representation technique for planar curves. One limitation of the well-known Curvature Scale Space (CSS) method is that it uses only curvature zero-crossings to characterize shapes and thus there is no CSS descriptor for convex shapes. The proposed method, on the other hand, uses bidimentional→unidimentional→bidimentional transformations together with resampling techniques to retain the full curvature information for shape characterization. It also employs the correlation coefficient as a measure of similarity. In the evaluation tests, the proposed method achieved a high correct classification rate (CCR), even when the shapes were severely corrupted by noise. Results clearly showed that the proposed method is more robust to noise than CSS.