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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Randomized neural network based descriptors for shape classification

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Autor(es):
de Mesquita Sa Junior, Jarbas Joaci [1, 2] ; Backes, Andre Ricardo [3] ; Bruno, Odemir Martinez [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Fed Ceara, Dept Comp Engn, Campus Sobral, Rua Estanislau Frota 563, BR-62010560 Sobral, CE - Brazil
[3] Univ Fed Uberlandia, Sch Comp Sci, Av Joao Naves de Avila 2121, BR-38408100 Uberlandia, MG - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: Neurocomputing; v. 312, p. 201-209, OCT 27 2018.
Citações Web of Science: 3
Resumo

Shape analysis is a very important field in computer vision. This work presents a novel and highly discriminative shape analysis method based on the weights of a Randomized Neural Network (RNN). Two approaches are proposed to extract the contour signature: Neighborhood approach uses the distance of each contour pixel and its immediate neighboring pixels and Contour portion approach, which uses metrics computed from contour sections to model the shape as RNN. We also proposed a signature that combines the feature vectors resulting from both approaches, thus resulting in a set of features tolerant to affine transformations, such as rotation and scale. We compared our approach with other shape analysis methods in 6 different shapes datasets. We calculated the accuracy as measure performance and obtained 97.98%, 99.07%, 84.67%, 87.67%, 88.92% and 80.58% for Kimia, Fish, Leaf, Rotated Leaf, Scaled Leaf and Noised Leaf datasets, respectively. The achieved performance of our method surpassed the results of several compared methods in most of these datasets, thus proving that our proposed signature can be applied successfully in shape analysis problems. (C) 2018 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 16/18809-9 - Deep learning e redes complexas aplicados em visão computacional
Beneficiário:Odemir Martinez Bruno
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal
Beneficiário:Odemir Martinez Bruno
Linha de fomento: Auxílio à Pesquisa - Regular