Authors:
Leonardo Chang
1
;
Miguel Arias-Estrada
2
;
L. Enrique Sucar
2
and
José Hernández-Palancar
3
Affiliations:
1
Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) and Advanced Technologies Application Center (CENATAV), Mexico
;
2
Instituto Nacional de Astrofísica and Óptica y Electrónica (INAOE), Mexico
;
3
Advanced Technologies Application Center (CENATAV), Cuba
Keyword(s):
Shape Matching, Invariant Shape Features, Shape Occlusion.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Object Recognition
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
Abstract:
In this work an invariant shape features extraction, description and matching method (LISF) for binary images
is proposed. In order to balance the discriminative power and the robustness to noise and occlusion in the
contour, local features are extracted from contour to describe shape, which are later matched globally. The proposed
extraction, description and matching methods are invariant to rotation, translation, and scale and present
certain robustness to partial occlusion. Its invariability and robustness are validated by the performed experiments
in shape retrieval and classification tasks. Experiments were carried out in the Shape99, Shape216, and
MPEG-7 datasets, where different artifacts were artificially added to obtain partial occlusion as high as 60%.
For the highest occlusion levels the proposed method outperformed other popular shape description methods,
with about 20% higher bull’s eye score and 25% higher accuracy in classification.