@article{charbonneau_role_2021,

title = {The role of spatial frequencies for facial pain categorization},

author = {I. Charbonneau and J. Guérette and S. Cormier and C. Blais and G. Lalonde-Beaudoin and F. W. Smith and D. Fiset},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111138273&doi=10.1038%2fs41598-021-93776-7&partnerID=40&md5=d759d0218de65fce371bb51d7f2593d8},

doi = {10.1038/s41598-021-93776-7},

issn = {20452322},

year  = {2021},

date = {2021-01-01},

journal = {Scientific Reports},

volume = {11},

number = {1},

abstract = {Studies on low-level visual information underlying pain categorization have led to inconsistent findings. Some show an advantage for low spatial frequency information (SFs) and others a preponderance of mid SFs. This study aims to clarify this gap in knowledge since these results have different theoretical and practical implications, such as how far away an observer can be in order to categorize pain. This study addresses this question by using two complementary methods: a data-driven method without a priori expectations about the most useful SFs for pain recognition and a more ecological method that simulates the distance of stimuli presentation. We reveal a broad range of important SFs for pain recognition starting from low to relatively high SFs and showed that performance is optimal in a short to medium distance (1.2–4.8 m) but declines significantly when mid SFs are no longer available. This study reconciles previous results that show an advantage of LSFs over HSFs when using arbitrary cutoffs, but above all reveal the prominent role of mid-SFs for pain recognition across two complementary experimental tasks. © 2021, The Author(s).},

note = {Publisher: Nature Research},

keywords = {Adolescent, adult, Classification, Distance Perception, emotion, Emotions, Face, face pain, Facial Expression, Facial Pain, Facial Recognition, female, human, Humans, Knowledge, male, Normal Distribution, Pattern Recognition, procedures, psychology, Psychophysics, recognition, reproducibility, Reproducibility of Results, Visual, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@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}

}