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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Fusion of complex networks and randomized neural networks for texture analysis

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Ribas, Lucas C. [1, 2] ; Sa Junior, Jarbas Joaci de Mesquita [3] ; Scabini, Leonardo F. S. [2] ; Bruno, Odemir M. [1, 2]
Total Authors: 4
[1] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense, Sao Carlos 13566590, SP - Brazil
[2] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, Sao Carlos 13560970, SP - Brazil
[3] Univ Fed Ceara, Curso Engn Computacao, Programa Posgrad Engn Eletr & Computacao, Campus Sobral, Rua Coronel Estanislau Frota 563, Sobral 62010560, CE - Brazil
Total Affiliations: 3
Document type: Journal article
Source: PATTERN RECOGNITION; v. 103, JUL 2020.
Web of Science Citations: 0

This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex network and its topological properties as well as the image pixels are used to train randomized neural networks to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracy of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field. (C) 2019 Published by Elsevier Ltd. (AU)

FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
Grantee:Lucas Correia Ribas
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 16/18809-9 - Deep learning and complex networks applied to computer vision
Grantee:Odemir Martinez Bruno
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support type: Regular Research Grants