Discrete Schroedinger transform for texture recognition

Autor(es)

Florindo, Joao Batista ; Bruno, Odemir Martinez

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION SCIENCES; v. 415, p. 142-155, NOV 2017.
• Citações Web of Science: 3

Resumo

This work presents a new procedure to extract features of grey-level texture images based on the discrete Schroedinger transform. This is a non-linear transform where the image is mapped as the initial probability distribution of a wave function and such distribution evolves in time following the Schroedinger equation from Quantum Mechanics. The features are provided by standard deviation of the distribution measured at different times. The proposed method is applied to the classification of three databases of textures used for benchmark and compared to other well-known texture descriptors in the literature, such as textons, local binary patterns, multifractals, among others. All of them are outperformed by the proposed method in terms of percentage of images correctly classified. The proposal is also applied to the identification of plant species using scanned images of leaves and again it outperforms other texture methods. A test with images affected by Gaussian and ``salt \& pepper{''} noise is also carried out, also with the best performance achieved by the Schroedinger descriptors. (C) 2017 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 16/16060-0 - Reconhecimento de Padrões em Imagens Baseado em Sistemas Complexos
• Beneficiário: Joao Batista Florindo
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/19143-3 - Geometria Fractal e Análise de Imagens Aplicadas à Biologia Vegetal
• Beneficiário: Joao Batista Florindo
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal
• Beneficiário: Odemir Martinez Bruno
• Modalidade de apoio: Auxílio à Pesquisa - Regular