Bayesian inference for signal segmentation in dyed textile images

• Grant number: 17/25469-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2018
• End date: August 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
• Principal Investigator: Nancy Lopes Garcia
• Grantee: Victor Freguglia Souza
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 17/10555-0 - Stochastic modeling of interacting systems, AP.TEM

Abstract

Markov random fields are often used when analyzing image data. In some applications, these analyses become difficult due to the presence of textures in images, which hinders the use of spatial models with simple interaction neighborhoods because they induce very sophisticated correlation structures. The main goals of the project are (i) to study and develop probabilistic methods for identifying the most sparse possible interaction structures for texture images; (ii) to study and develop estimation methods for these models in a Bayesian approach; (iii) apply the developed theory to real textile dyeing data that motivate the project. (AU)

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

FREGUGLIA, VICTOR; GARCIA, NANCY L.; BICAS, JULIANO L.. Hidden Markov random field models applied to color homogeneity evaluation in dyed textile images. ENVIRONMETRICS, v. 31, n. 4, DEC 2019. (17/25469-2, 17/10555-0)

FREGUGLIA, VICTOR; GARCIA, NANCY LOPES. nference Tools for Markov Random Fields on Lattices: The R Package mrf2. JOURNAL OF STATISTICAL SOFTWARE, v. 101, n. 8, p. 1-36, JAN 2022. (17/25469-2)

FREGUGLIA, VICTOR; GARCIA, NANCY L.. Detecting renewal states in chains of variable length via intrinsic Bayes factors. STATISTICS AND COMPUTING, v. 33, n. 1, p. 17-pg., 2023-02-01. (17/25469-2, 17/10555-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SOUZA, Victor Freguglia. Seleção bayesiana de estruturas de dependência para processos markovianos. 2022. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica Campinas, SP.