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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.)

Randomized neural network based descriptors for shape classification

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Author(s):
de Mesquita Sa Junior, Jarbas Joaci [1, 2] ; Backes, Andre Ricardo [3] ; Bruno, Odemir Martinez [1]
Total Authors: 3
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: Neurocomputing; v. 312, p. 201-209, OCT 27 2018.
Web of Science Citations: 3
Abstract

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)

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