Discrete Schroedinger transform for texture recognition

Author(s)

Florindo, Joao Batista ; Bruno, Odemir Martinez

• 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