@article{allili_texture_2014,

title = {Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications},

author = {M. S. Allili and N. Baaziz and M. Mejri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896903467&doi=10.1109%2fTMM.2014.2298832&partnerID=40&md5=16b2fa741e71e1e581f6b0f54c43a676},

doi = {10.1109/TMM.2014.2298832},

issn = {15209210},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Multimedia},

volume = {16},

number = {3},

pages = {772–784},

abstract = {In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions (MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT (SCT) and redundant CT (RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods. © 2014 IEEE.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Contourlet coefficients, Contourlet transform, Defects, Directional information, Fabric texture, face recognition, Generalized Gaussian Distributions, Inspection, Mixtures, Probability density function, Probability density functions (PDFs), State-of-the-art methods, Texture retrieval, Textures},

pubstate = {published},

tppubtype = {article}

}

@article{allili_contourlet-based_2011,

title = {Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions},

author = {M. S. Allili and N. Baaziz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052796353&doi=10.1007%2f978-3-642-23678-5_53&partnerID=40&md5=fde8aaeea1609c81747b0ab27a8c78ce},

doi = {10.1007/978-3-642-23678-5_53},

issn = {03029743},

year  = {2011},

date = {2011-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {6855 LNCS},

number = {PART 2},

pages = {446–454},

abstract = {We address the texture retrieval problem using contourlet-based statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling. © 2011 Springer-Verlag.},

note = {ISBN: 9783642236778 

Place: Seville},

keywords = {Contourlet transform, Contourlets, Distribution modelling, Finite mixtures, Gaussian distribution, Generalized Gaussian Distributions, Image analysis, Kullback-Leibler divergence, Mixtures, Monte-Carlo sampling, Probability density function, Similarity measure, Statistical representations, Texture discrimination, Texture retrieval, Textures},

pubstate = {published},

tppubtype = {article}

}