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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.)

Discrete Schroedinger transform for texture recognition

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Author(s):
Florindo, Joao Batista ; Bruno, Odemir Martinez
Total Authors: 2
Document type: Journal article
Source: INFORMATION SCIENCES; v. 415, p. 142-155, NOV 2017.
Web of Science Citations: 3
Abstract

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)

FAPESP's process: 16/16060-0 - Pattern recognition on images based on complex systems
Grantee:Joao Batista Florindo
Support type: Regular Research Grants
FAPESP's process: 12/19143-3 - Fractal geometry and image analysis applied to vegetal biology
Grantee:Joao Batista Florindo
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support type: Regular Research Grants