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Pattern Recognition on Images Based on Complex Systems


This project proposes the study and development of mathematical and computational tools for image analysis based on complex systems. More specifically, the researcher will focus on three approaches, namely, fractal geometry, complex networks and methods of Mathematical Physics. All these techniques share the ability of describing with great accuracy the irregularity or homogeneity of an object represented in an image viewed from different scale levels. This is crucial information even for our vision system, which frequently uses this information to distinguish objects and scenery around us. The same importance of this attribute can be observed in computer vision systems, particularly in the analysis of nature images, as demonstrated in several studies previously developed by the applicant. Finally, the techniques developed here will be applied to real-world problems involving biological images, especially in plant taxonomy and phenotyping. It is also worth noting that this project aims to create a symbiosis between application and theory and so both theories developed for general purpose applications will be tested on specific problems as well as such problems will also give rise to new theoretical lines that can meet their needs. This paper describes the theoretical lines and involved applications, as well as the expected scientific production and potential qualification of human resources. (AU)

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Scientific publications (10)
(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)
TARASCHI, GIOVANNI; FLORINDO, JOAO B.. Computing fractal descriptors of texture images using sliding boxes: An application to the identification of Brazilian plant species. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 545, . (16/16060-0)
SILVA, PEDRO M.; FLORINDO, JOAO B.. Fractal measures of image local features: an application to texture recognition. MULTIMEDIA TOOLS AND APPLICATIONS, v. 80, n. 9, p. 14213-14229, . (16/16060-0)
FLORINDO, JOAO BATISTA; BRUNO, ODEMIR MARTINEZ. Discrete Schroedinger transform for texture recognition. INFORMATION SCIENCES, v. 415, p. 142-155, . (16/16060-0, 14/08026-1, 12/19143-3)
SILVA, PEDRO M.; FLORINDO, JOAO B.. A statistical descriptor for texture images based on the box counting fractal dimension. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 528, . (16/16060-0)
FLORINDO, JOAO BATISTA; CASANOVA, DALCIMAR; BRUNO, ODEMIR MARTINEZ. A Gaussian pyramid approach to Bouligand-Minkowski fractal descriptors. INFORMATION SCIENCES, v. 459, p. 36-52, . (16/16060-0, 14/08026-1, 12/19143-3, 13/22205-3)
FLORINDO, JOAO B.; BRUNO, ODEMIR M.. Texture classification using non-Euclidean Minkowski dilation. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 493, p. 189-202, . (16/16060-0, 14/08026-1, 12/19143-3)
METZE, KONRADIN; ADAM, RANDALL; FLORINDO, JOAO BATISTA. The fractal dimension of chromatin - a potential molecular marker for carcinogenesis, tumor progression and prognosis. EXPERT REVIEW OF MOLECULAR DIAGNOSTICS, v. 19, n. 4, p. 299-312, . (16/16060-0)
FLORINDO, JOAO BATISTA; BRUNO, ODEMIR MARTINEZ. Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension. Journal of Mathematical Imaging and Vision, v. 61, n. 1, p. 140-159, . (13/22205-3, 16/16060-0, 12/19143-3, 14/08026-1)
SILVA, PEDRO M.; FLORINDO, JOAO B.. Using down-sampling for multiscale analysis of texture images. PATTERN RECOGNITION LETTERS, v. 125, p. 411-417, . (16/16060-0)
FLORINDO, JOAO B.; LEE, YOUNG-SUP; JUN, KYUNGKOO; JEON, GWANGGIL; ALBERTINI, MARCELO K.. VisGraphNet: A complex network interpretation of convolutional neural features. INFORMATION SCIENCES, v. 543, p. 296-308, . (16/16060-0)

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