Object Predetection Based on Kernel Parametric Distribution Fitting
Abstract
Multimodal distribution fitting is an important task in pattern recognition. For instance,
the predetection which is the preliminary stage that limits image areas to be processed in the
detection stage amounts to the modeling of a multimodal distribution. Different techniques are available for
such modeling. We propose a pros and cons analysis of multimodal distribution fitting
techniques convenient for object predetection in images. This analysis leads us to propose efficient and
accurate variants over the previously proposed techniques as shown by our experiments. These variants are
based on parametric distribution fitting in the RKHS space induced by a positive definite kernel.
Reference
@inproceedings{jpt-icpr06,
author = {Tarel, J.-P. and Boughorbel, S.},
title = {Object Predetection Based on Kernel Parametric Distribution Fitting},
booktitle = {Proceedings of International Conference on Pattern Recognition (ICPR'06)},
date = {August 20-24},
address = {Hong Kong, China},
pages = {808-811},
year = {2006},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/icpr06.html}
}
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