Dr Alejandro Frery, Universidade Federal de Alagoas.
Data Analysis with Statistical Information Theory
Statistical Information Theory produces, among other tools,
statistical tests to verify if two or more samples come from the same
distribution. This is a classical problem in Statistics, and the KS,
t, chi-squared, F, and likelihood ratio tests are some of the basic
tools one has to solve it. Although widespread due to their good
performance, these tests are limited to relatively simple situations,
and their discrimination ability may be severely impaired when the
data do not follow the hypothesis under which they were derived. Many
applications rely on the use of measures of dissimilarity, oftentimes
distances, to decide if two or more samples were produced by the same
probability law. Distances are flexible tools, but they seldom produce
rich semantic results: "near vs. far" is less expressive than "reject
vs. do not reject the hypothesis that the samples come from the same
distribution". Recent results from the Statistical Information Theory
realm provide powerful tools by endowing a large class of distances
with semantic contents. This is the case of the stochastic h-phi
distances, and the differences of h-phi entropies. These families of
test statistics are a promising venue for both theoretical studies and
applications. In this talk we will revise the main concepts underlying
these new tools, and we will see how eight seemingly different
problems in image processing and analysis (feature extraction,
hierarchical segmentation, classification, noise reduction, spectral
decomposition, parameter estimation, edge and change detection) can be
formulated and solved within this context.
Dr Frery received the Engineer's Degree in Electronics and Electricity from the Facultad de Ingeniería, Universidad de Mendoza, Argentina in 1985. His MSc degree is in Applied Mathematics (Statistics) from the Instituto de Matemática Pura e Aplicada (IMPA), Rio de Janeiro, RJ, Brazil, obtained in 1990. His PhD degree is in Applied Computing from the Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil, obtained in 1993. During both his MSc and PhD he worked with statistical models for image simulation, processing, and analysis: Markov random fields and models for synthetic aperture radar (SAR) images. His current research interests, besides image processing and analysis, include computational statistics and modelling of complex systems.
Dr Millaray Curilem, Universidad de La Frontera, Chile
Seismic Pattern Recognition in volcanoes
The talk presents the main results of a joint project developed by the Volcanological Observatory of Southern Andes (OVDAS) and the University of La Frontera, in partnership with the University of Chile and the University of Santiago, Chile. The project aims to support the monitoring of volcanic seismicity of Llaima volcano, one of the most active volcanoes in South America. In this way, the project aims to create a uniform approach that automates the identification of seismic events and organizes the seismic signals, by developing a platform for computer aided management. The main advances in volcano monitoring network in our country are presented, as well as the main challenges and difficulties of seismic monitoring. The methodology followed in the development of the project involves several stages, however, one of the most important is the automatic seism identification step. Some volcanic seisms are representative of internal processes of the volcano, this is why their identification is highly relevant. Techniques of signal processing and computational intelligence are used to accomplish these tasks. The results are encouraging, but several challenges remain to be solved in this interesting application of pattern recognition techniques.
Millaray Curilem received the B.E. degree in electrical engineering from the Instituto Superior Politécnico, José Antonio Echeverría, Cuba, in 1991, and the Dr. degree in Electrical Engineering from the Universidade Federal de Santa Catarina, Brazil, in 2002. In 1994, she joined the Department of Electrical Engineering, of the Universidad de La Frontera, at Temuco, Chile, as a Lecturer, and in 2009 she became Associate Professor of this University. She performed a post-doctoral research in the Department of Informatics at the Universidad de Santiago de Chile (2007). Her current research interests include Computational Intelligence applied to pattern recognition of seismic signals from volcanoes, biomedical engineering and education. Dr. Curilem is a Fellow Member of the Computational Intelligence Society of the IEEE. She participated in the Organizing Committee of the Iberoamerican Congress on Pattern Recognition (CIARP 2011) and the Chilean Workshop on Pattern Recognition (CWPR 2010-2011). Dr. Curilem has many ISI, Scopus and SciELO publications and she participates in several national and institutional research projects as main or co-researcher.