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Artificial vision and pattern recognition applied to vegetal plasticity

Grant number: 14/08026-1
Support type:Regular Research Grants
Duration: September 01, 2016 - August 31, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Odemir Martinez Bruno
Grantee:
Home Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos, SP, Brazil
Assoc. researchers:Alexandre Souto Martinez ; Antoine Manzanera ; Antoine Vacavant ; Davi Rodrigo Rossatto ; Francisco Eduardo Gontijo Guimaraes ; Jan Marcel Baetens ; Joao Batista Florindo ; Luís Carlos Bernacci ; Marcos Silveira Buckeridge ; Pedro Henrique de Cerqueira Luz ; Rosana Marta Kolb

Abstract

Phenotypic plasticity is the ability of an organism to express different phenotypes depending on the biotic and abiotic environment. In plants, phenotypic plasticity is the ability to change your physiology or morphology according to the ambient conditions. This project has the objective to use computer vision and pattern recognition methods to analyze the phenotypic plasticity of plants through images of leaf surface and histologic sections of the leaf organs.These images are presented as characteristic nonlinearity, and the presence of complex phenomena. Thus, methods are based on complex systems used for analysis. They are considered in this design methods based on fractal geometry, complex networks and automata. The use of methods based on complex systems in images and pattern recognition is the proponent of specialty investigating the issue for more than a decade.The applications of foliar plasticity analysis are diverse and range from theoretical areas such as botany and plant physiology to applied ones as biotechnology and agriculture. (AU)

Scientific publications
(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)
MACHICAO, JEANETH; BRUNO, ODEMIR M. Improving the pseudo-randomness properties of chaotic maps using deep-zoom. Chaos, v. 27, n. 5 MAY 2017. Web of Science Citations: 0.
FLORINDO, JOAO B.; BRUNO, ODEMIR M.; LANDINI, GABRIEL. Morphological classification of odontogenic keratocysts using Bouligand-Minkowski fractal descriptors. COMPUTERS IN BIOLOGY AND MEDICINE, v. 81, p. 1-10, FEB 1 2017. Web of Science Citations: 1.
BARBONI MIRANDA, GISELE HELENA; MACHICAO, JEANETH; BRUNO, ODEMIR MARTINEZ. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks. SCIENTIFIC REPORTS, v. 6, NOV 22 2016. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.