de Recherche et d’Innovation
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Boulmerka, A.; Allili, M. Saïd; Ait-Aoudia, S.
A generalized multiclass histogram thresholding approach based on mixture modelling Journal Article
In: Pattern Recognition, vol. 47, no. 3, pp. 1330–1348, 2014, ISSN: 00313203.
Abstract | Links | BibTeX | Tags: Arbitrary number, Conditional distribution, Gaussian distribution, Gaussian noise (electronic), Generalized Gaussian Distributions, Graphic methods, Histogram thresholding, Image segmentation, Minimum error thresholding, Mixture-modelling, Mixtures, State-of-the-art techniques, Statistical methods, Thresholding, Thresholding methods
@article{boulmerka_generalized_2014,
title = {A generalized multiclass histogram thresholding approach based on mixture modelling},
author = {A. Boulmerka and M. Saïd Allili and S. Ait-Aoudia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888328869&doi=10.1016%2fj.patcog.2013.09.004&partnerID=40&md5=d8b872bd0abe9e6c4d52439f8ec360bc},
doi = {10.1016/j.patcog.2013.09.004},
issn = {00313203},
year = {2014},
date = {2014-01-01},
journal = {Pattern Recognition},
volume = {47},
number = {3},
pages = {1330–1348},
abstract = {This paper presents a new approach to multi-class thresholding-based segmentation. It considerably improves existing thresholding methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions using mixtures of generalized Gaussian distributions (MoGG). The proposed approach seamlessly: (1) extends the standard Otsu's method to arbitrary numbers of thresholds and (2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. MoGGs enable efficient representation of heavy-tailed data and multi-modal histograms with flat or sharply shaped peaks. Experiments on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2013 Elsevier Ltd. All rights reserved.},
keywords = {Arbitrary number, Conditional distribution, Gaussian distribution, Gaussian noise (electronic), Generalized Gaussian Distributions, Graphic methods, Histogram thresholding, Image segmentation, Minimum error thresholding, Mixture-modelling, Mixtures, State-of-the-art techniques, Statistical methods, Thresholding, Thresholding methods},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.
Wavelet-based texture retrieval using a mixture of generalized Gaussian distributions Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, pp. 3143–3146, Istanbul, 2010, ISBN: 978-0-7695-4109-9, (ISSN: 10514651).
Abstract | Links | BibTeX | Tags: Avelet decomposition, Gaussian distribution, Generalized Gaussian Distributions, Image retrieval, KLD, Kullback-Leibler distance, Marginal distribution, Metropolis-Hastings samplings, Mixtures, Pattern Recognition, Probability density function, Probability density function (pdf), Similarity measurements, Statistical methods, Statistical scheme, Texture discrimination, Texture energy, Texture image retrieval, Texture retrieval, Textures, Wavelet coefficients, Wavelet representation
@inproceedings{allili_wavelet-based_2010,
title = {Wavelet-based texture retrieval using a mixture of generalized Gaussian distributions},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149489822&doi=10.1109%2fICPR.2010.769&partnerID=40&md5=bf29f6057b57f85a0d83ac16bb4afaf5},
doi = {10.1109/ICPR.2010.769},
isbn = {978-0-7695-4109-9},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
pages = {3143–3146},
address = {Istanbul},
abstract = {In this paper, we address the texture retrieval problem using wavelet distribution. We propose a new statistical scheme to represent the marginal distribution of the wavelet coefficients using a mixture of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of histogram shapes, which provides a better description of texture and enhances texture discrimination. We propose a similarity measurement based on Kullback-Leibler distance (KLD), which is calculated using MCMC Metropolis-Hastings sampling algorithm. We show that our approach yields better texture retrieval results than previous methods using only a single probability density function (pdf) for wavelet representation, or texture energy distribution. © 2010 IEEE.},
note = {ISSN: 10514651},
keywords = {Avelet decomposition, Gaussian distribution, Generalized Gaussian Distributions, Image retrieval, KLD, Kullback-Leibler distance, Marginal distribution, Metropolis-Hastings samplings, Mixtures, Pattern Recognition, Probability density function, Probability density function (pdf), Similarity measurements, Statistical methods, Statistical scheme, Texture discrimination, Texture energy, Texture image retrieval, Texture retrieval, Textures, Wavelet coefficients, Wavelet representation},
pubstate = {published},
tppubtype = {inproceedings}
}
Bouchard, S.; Robillard, G.; St-Jacques, J.; Dumoulin, S.; Patry, M. -J.; Renaud, P.
Reliability and validity of a single-item measure of presence in VR Proceedings Article
In: Proceedings - 3rd IEEE International Workshop on Haptic, Audio and Visual Environments and their Applications - HAVE 2004, pp. 59–61, Ottawa, Ont., 2004, ISBN: 0-7803-8817-8 978-0-7803-8817-8.
Abstract | Links | BibTeX | Tags: Computer software, Education, Environmental distractions, Ergonomics, Human factors, Information technology, Item-response theory, Psychological Tests, reliability, Sensitivity analysis, Statistical methods, virtual reality
@inproceedings{bouchard_reliability_2004,
title = {Reliability and validity of a single-item measure of presence in VR},
author = {S. Bouchard and G. Robillard and J. St-Jacques and S. Dumoulin and M. -J. Patry and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-15944418961&partnerID=40&md5=7aff5eba0fac9d8ca8adeb0a40063473},
isbn = {0-7803-8817-8 978-0-7803-8817-8},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings - 3rd IEEE International Workshop on Haptic, Audio and Visual Environments and their Applications - HAVE 2004},
pages = {59–61},
address = {Ottawa, Ont.},
abstract = {Measuring presence reliably and with minimal intrusion manner is not easy. The present study reports on six studies that have validated a measure of presence consisting of only one item. The content, face validity, test-retest, convergent and divergent validity as well as sensitivity were all confirming reliability and validity of a single-item measure. ©2004 IEEE.},
keywords = {Computer software, Education, Environmental distractions, Ergonomics, Human factors, Information technology, Item-response theory, Psychological Tests, reliability, Sensitivity analysis, Statistical methods, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}