@article{allili_wavelet_2012,

title = {Wavelet modeling using finite mixtures of generalized Gaussian distributions: Application to texture discrimination and retrieval},

author = {M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859096106&doi=10.1109%2fTIP.2011.2170701&partnerID=40&md5=0420facdc04978ad84bea3126bc1183a},

doi = {10.1109/TIP.2011.2170701},

issn = {10577149},

year  = {2012},

date = {2012-01-01},

journal = {IEEE Transactions on Image Processing},

volume = {21},

number = {4},

pages = {1452–1464},

abstract = {This paper addresses statistical-based texture modeling using wavelets. We propose a new approach to represent the marginal distribution of the wavelet coefficients using finite mixtures of generalized Gaussian (MoGG) distributions. The MoGG captures a wide range of histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdf's), as proposed by recent state-of-the-art approaches. Moreover, we propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte Carlo sampling methods. Through experiments on two popular texture data sets, we show that our approach yields significant performance improvements for texture discrimination and retrieval, as compared with recent methods of statistical-based wavelet modeling. © 2011 IEEE.},

keywords = {algorithm, Algorithms, article, Automated, automated pattern recognition, computer assisted diagnosis, Computer Simulation, Computer-Assisted, Data Interpretation, Finite mixtures, Generalized Gaussian, Generalized Gaussian Distributions, Image Enhancement, Image Interpretation, Image segmentation, Imaging, Kullback Leibler divergence, Marginal distribution, methodology, Mixtures, Models, Monte Carlo methods, Monte Carlo sampling, Normal Distribution, Pattern Recognition, Performance improvements, reproducibility, Reproducibility of Results, Sensitivity and Specificity, Similarity measure, State-of-the-art approach, Statistical, statistical analysis, statistical model, Texture data set, Texture discrimination, Texture modeling, Textures, three dimensional imaging, Three-Dimensional, Wavelet Analysis, Wavelet coefficients, Wavelet decomposition, Wavelet modeling},

pubstate = {published},

tppubtype = {article}

}

@article{renaud_real-time_2011,

title = {Real-time functional magnetic imaging-brain-computer interface and virtual reality. promising tools for the treatment of pedophilia.},

author = {P. Renaud and C. Joyal and S. Stoleru and M. Goyette and N. Weiskopf and N. Birbaumer},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960222279&doi=10.1016%2fB978-0-444-53355-5.00014-2&partnerID=40&md5=5d87cef8357da4e209f81c7d19b9afa2},

doi = {10.1016/B978-0-444-53355-5.00014-2},

issn = {00796123},

year  = {2011},

date = {2011-01-01},

journal = {Progress in Brain Research},

volume = {192},

pages = {263–272},

abstract = {This chapter proposes a prospective view on using a real-time functional magnetic imaging (rt-fMRI) brain-computer interface (BCI) application as a new treatment for pedophilia. Neurofeedback mediated by interactive virtual stimuli is presented as the key process in this new BCI application. Results on the diagnostic discriminant power of virtual characters depicting sexual stimuli relevant to pedophilia are given. Finally, practical and ethical implications are briefly addressed. © 2011 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {anterior cingulate, book, brain computer interface, brain region, cognitive therapy, electroencephalogram, eye tracking, functional magnetic resonance imaging, human, neuroanatomy, Pedophilia, Plethysmography, priority journal, Sensitivity and Specificity, sexual arousal, Sexual Behavior, sexual crime, sexual deviation, virtual reality},

pubstate = {published},

tppubtype = {article}

}

@article{renaud_behavioral_2002,

title = {Behavioral avoidance dynamics in the presence of a virtual spider},

author = {P. Renaud and S. Bouchard and R. Proulx},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036708418&doi=10.1109%2fTITB.2002.802381&partnerID=40&md5=4dd582cb233d8396f7795cf93cb1b984},

doi = {10.1109/TITB.2002.802381},

issn = {10897771 (ISSN)},

year  = {2002},

date = {2002-01-01},

journal = {IEEE Transactions on Information Technology in Biomedicine},

volume = {6},

number = {3 SPEC.},

pages = {235–243},

abstract = {Tracking behavior with a virtual spider and a neutral target is compared in fearful and nonfearful subjects. Head-tracking in virtual environments appears to be a scale-free behavior with long-range fractal-like patterns. Moreover, these fractal patterns change according to what the target affords the tracker and the level of behavioral avoidance manifested by the subjects. Results are interpreted in terms of ecological psychology and nonlinear dynamics, and implications for virtual reality (VR) psychology are outlined.},

keywords = {adult, Affordance, animal, Animals, Anxiety, Anxiety disorder, article, Behavioral avoidance, Biomedical engineering, Chronic Disease, clinical trial, Computer Graphics, computer interface, Computer Simulation, controlled clinical trial, controlled study, Environment, Fear, female, Fractal dynamics, Fractals, head movement, Head movements, hospitalization, human, Human computer interaction, Humans, Interactive computer graphics, male, Motion Perception, movement perception, pathophysiology, Patient treatment, phobia, Phobic Disorders, Presence, psychological aspect, Psychomotor Performance, Psychophysiology, reproducibility, Reproducibility of Results, Self Assessment (Psychology), self evaluation, Sensitivity and Specificity, Severity of Illness Index, spider, Spiders, Tracking, treatment outcome, User-Computer Interface, virtual reality},

pubstate = {published},

tppubtype = {article}

}