@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}

}

@inproceedings{allili_robust_2006,

title = {A robust video object tracking by using active contours},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845513941&doi=10.1109%2fCVPRW.2006.20&partnerID=40&md5=64ff2be5c45a6c206420bf6eb5589bca},

doi = {10.1109/CVPRW.2006.20},

isbn = {0769526462 (ISBN); 978-076952646-1 (ISBN)},

year  = {2006},

date = {2006-01-01},

booktitle = {2006 Conference on Computer Vision and Pattern Recognition Workshops},

volume = {2006},

pages = {135},

publisher = {IEEE Computer Society},

address = {New York, NY},

abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.},

note = {Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops},

keywords = {Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking},

pubstate = {published},

tppubtype = {inproceedings}

}