ACCURATE SIMILARITY MEASURES FOR SILHOUETTES RECOGNITION

Saliha Aouat, Slimane Larabi

2012

Abstract

In this paper, we propose a new method to recognize silhouettes of objects. Models of silhouettes are stored in the database using their textual descriptors. Textual Descriptors are written following the part-based method published in (Larabi et al, 2003). The main issue with the textual description is its sensitiveness to noise, in order to overcome this issue, we have applied (Aouat and Larabi, 2010) a convolution to initial outline shape with a Gaussian filter at different scales. The approach was very interesting for shape matching and indexing (Aouat and Larabi, 2009), but unfortunately it is not appropriate to the recognition process because there is no use of similarity measures in order to select the best model for a query silhouette. In this paper, we compute parts areas and geometric quasi-invariants to find the best model for the given query; they are efficient similarity measures to perform the recognition process.

References

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Paper Citation


in Harvard Style

Aouat S. and Larabi S. (2012). ACCURATE SIMILARITY MEASURES FOR SILHOUETTES RECOGNITION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 397-400. DOI: 10.5220/0003815303970400


in Bibtex Style

@conference{visapp12,
author={Saliha Aouat and Slimane Larabi},
title={ACCURATE SIMILARITY MEASURES FOR SILHOUETTES RECOGNITION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={397-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003815303970400},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - ACCURATE SIMILARITY MEASURES FOR SILHOUETTES RECOGNITION
SN - 978-989-8565-03-7
AU - Aouat S.
AU - Larabi S.
PY - 2012
SP - 397
EP - 400
DO - 10.5220/0003815303970400