| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Camp Nas, Inst Math Stat & Sci Comp, Rua Sergio Buarque Holanda 651, BR-13083859 Campinas, SP - Brazil
[2] Fed Inst Educ Sci & Technol Espirito Santo, Rodovia Governador Jose Sete 184, BR-29150410 Cariacica, ES - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | PATTERN RECOGNITION LETTERS; v. 125, p. 411-417, JUL 1 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
This work proposes the study of a simple yet efficient strategy to accomplish multiscale analysis of gray level images. The method is applied to the classification of texture/material images. The proposal employs histograms of down-sampled versions of the initial image to compose a feature vector. A statistical model based on Markov random fields is also developed and employed to explain how the information conveyed by the proposed descriptors can express degrees of homo/heterogeneity in the image. This is widely known to be a fundamental feature in texture analysis. The accuracy of our descriptors is compared to several state-of-the-art approaches and the achieved results confirm our expectation that a straightforward method can be efficiently employed even in the recognition of objects in large and complex databases. (C) 2019 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 16/16060-0 - Pattern Recognition on Images Based on Complex Systems |
| Grantee: | Joao Batista Florindo |
| Support Opportunities: | Regular Research Grants |