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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.)

Texture descriptor based on partially self-avoiding deterministic walker on networks

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Author(s):
Goncalves, Wesley Nunes [1] ; Backes, Andre Ricardo [2] ; Martinez, Alexandre Souto [3] ; Bruno, Odemir Martinez [1]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, IFSC, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Fed Uberlandia, Fac Comp, BR-38408100 Uberlandia, MG - Brazil
[3] Univ Sao Paulo, FFCLRP, BR-14040901 Ribeirao Preto, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 39, n. 15, p. 11818-11829, NOV 1 2012.
Web of Science Citations: 11
Abstract

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 11/01523-1 - Computer vision methods applied to the identification and analysis of plants
Grantee:Odemir Martinez Bruno
Support Opportunities: Regular Research Grants
FAPESP's process: 10/08614-0 - Static and Dynamic Texture Analysis and their Applications in Biology and Nanotechnology
Grantee:Wesley Nunes Gonçalves
Support Opportunities: Scholarships in Brazil - Doctorate