Modeling and analysis of complex networks for computer vision

• Grant number: 16/23763-8
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): June 01, 2017
• Effective date (End): November 30, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal researcher: Odemir Martinez Bruno
• Grantee: Lucas Correia Ribas
• Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 19/03277-0 - Pattern Recognition in Complex Networks using Distance Transform, BE.EP.DR

Abstract

Complex networks have been used as a study tool and contributed in several areas of science due to their interdisciplinary nature and conceptual simplicity. In computer science, networks have been used in problems of architecture, artificial intelligence, computer networks and computer vision. With regard to computational vision, several approaches have been proposed over the past decade to pattern recognition based on complex networks. This fact is motivated by the nonlinear nature that many images present, making the use of complex networks a potential tool as a measure of complexity. Most of these approaches were proposed by researchers from USP of São Carlos. The results show great potential in the use of complex networks as a tool for the development of new methods for computer vision. The objective of this doctorate is to study and develop new methods based on complex networks for computational vision. In particular, two problems of computational vision that are the specialty of the research group and that has been studied for more than a decade will be investigated: texture and shape analysis. In this way, we intend to investigate new methodologies for the modeling of images/videos in networks and new methods of network analysis. In particular, we will investigate the use of automata and deep learning as new analysis tools. In addition, existing approaches will be studied in order to identify possible limitations and when possible, improvements will be proposed. As a way of analyzing the potential of the approaches developed, real problems of biology and nanotechnology in which the researcher's research group is working will be considered for application. In this way, this project presents a great possibility of contribution with new methods for the area of computational vision and, consequently, for the areas of biology and nanotechnology. (AU)

Scientific publications

(11)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SCABINI, LEONARDO F. S.; RIBAS, LUCAS C.; NEIVA, MARIANE B.; JUNIOR, ALTAMIR G. B.; FARFAN, ALEX J. F.; BRUNO, ODEMIR M. Social interaction layers in complex networks for the dynamical epidemic modeling of COVID-19 in Brazil. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 564, FEB 15 2021. Web of Science Citations: 0.

RODRIGUES, VALQUIRIA C.; SOARES, JULIANA C.; SOARES, ANDREY C.; BRAZ, DANIEL C.; MELENDEZ, MATIAS ELISEO; RIBAS, LUCAS C.; SCABINI, LEONARDO F. S.; BRUNO, ODEMIR M.; CARVALHO, ANDRE LOPES; REIS, RUI MANUEL; SANFELICE, RAFAELA C.; OLIVEIRA, JR., OSVALDO N. Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3. Talanta, v. 222, JAN 15 2021. Web of Science Citations: 0.

RIBAS, LUCAS C.; SA JUNIOR, JARBAS JOACI DE MESQUITA; SCABINI, LEONARDO F. S.; BRUNO, ODEMIR M. Fusion of complex networks and randomized neural networks for texture analysis. PATTERN RECOGNITION, v. 103, JUL 2020. Web of Science Citations: 0.

RIBAS, LUCAS C.; MACHICAO, JEANETH; BRUNO, ODEMIR M. Life-Like Network Automata descriptor based on binary patterns for network classification. INFORMATION SCIENCES, v. 515, p. 156-168, APR 2020. Web of Science Citations: 0.

SCABINI, LEONARDO F. S.; RIBAS, LUCAS C.; BRUNO, ODEMIR M. Spatio-spectral networks for color-texture analysis. INFORMATION SCIENCES, v. 515, p. 64-79, APR 2020. Web of Science Citations: 0.

RIBAS, LUCAS C.; BRUNO, ODEMIR M. Dynamic texture analysis using networks generated by deterministic partially self-avoiding walks. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 541, MAR 1 2020. Web of Science Citations: 0.

DE MESQUITA SA JUNIOR, JARBAS JOACI; RIBAS, LUCAS CORREIA; BRUNO, ODEMIR MARTINEZ. Randomized neural network based signature for dynamic texture classification. EXPERT SYSTEMS WITH APPLICATIONS, v. 135, p. 194-200, NOV 30 2019. Web of Science Citations: 1.

RIBAS, LUCAS C.; GONCALVES, WESLEY N.; BRUNO, ODEMIR M. Dynamic texture analysis with diffusion in networks. DIGITAL SIGNAL PROCESSING, v. 92, p. 109-126, SEP 2019. Web of Science Citations: 0.

RIBAS, LUCAS CORREIA; GONCALVES, DIOGO NUNES; SILVA, JONATHAN DE ANDRADE; DE CASTRO, JR., AMAURY ANTONIO; BRUNO, ODEMIR MARTINEZ; GONCALVES, WESLEY NUNES. Fractal dimension of bag-of-visual words. PATTERN ANALYSIS AND APPLICATIONS, v. 22, n. 1, p. 89-98, FEB 2019. Web of Science Citations: 1.

RIBAS, LUCAS CORREIA; NEIVA, MARIANE BARROS; BRUNO, ODEMIR MARTINEZ. Distance transform network for shape analysis. INFORMATION SCIENCES, v. 470, p. 28-42, JAN 2019. Web of Science Citations: 1.

MACHICAO, JEANETH; RIBAS, LUCAS C.; SCABINI, LEONARDO F. S.; BRUNO, ODERMIR M. Cellular automata rule characterization and classification using texture descriptors. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 497, p. 109-117, MAY 1 2018. Web of Science Citations: 2.