Authors:
Eiji Yoshida
and
Seiichi Mita
Affiliation:
To Toyota Technological Institute 2-12-1 Hisakata, Japan
Keyword(s):
Shape matching, curvature, shape classification, shape similarity measure.
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 propose a new method for the measurement of shape similarity. Our proposed method encodes the contour of an object by using the curvature of the object. If one objects are similar (under translation, rotation, and scaling) in shape to the other, these codes themselves or their cyclic shift have the same values. We compare our method with other methods such as CSS (curvature scale space), and shape context. We show that the recognition rate of our method is 100 % and 90.40 % for the rotation and scaling robustness test using
MPEG7-CE-Shape1 and 81.82 % and 95.14 % for the similarity-based retrieval test and the occlusion test using Kimia's silhouette. In particular, the value of the occlusion test is approximately 25 % higher than those of CSS, SC. Moreover, we show that the computational cost of our method is not so large by comparison our method with above methods.