dc.creator | Gonçalves, Wesley Nunes | |
dc.creator | Bruno, Odemir Martinez | |
dc.date.accessioned | 2014-05-29T14:13:32Z | |
dc.date.accessioned | 2018-07-04T16:45:44Z | |
dc.date.available | 2014-05-29T14:13:32Z | |
dc.date.available | 2018-07-04T16:45:44Z | |
dc.date.created | 2014-05-29T14:13:32Z | |
dc.date.issued | 2013-11 | |
dc.identifier | Pattern Recognition, Amsterdam : Elsevier,v. 46, n. 11, p. 2953-2968, Nov. 2013 | |
dc.identifier | 0031-3203 | |
dc.identifier | http://www.producao.usp.br/handle/BDPI/45123 | |
dc.identifier | 10.1016/j.patcog.2013.03.012 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1640020 | |
dc.description.abstract | In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets. | |
dc.language | eng | |
dc.publisher | Elsevier BV | |
dc.publisher | Amsterdam | |
dc.relation | Pattern Recognition | |
dc.rights | Elsevier Ltd | |
dc.rights | restrictedAccess | |
dc.subject | Fractal dimension | |
dc.subject | Texture | |
dc.subject | Pattern recognition | |
dc.subject | Texture analysis | |
dc.subject | Deterministic walkers | |
dc.title | Combining fractal and deterministic walkers for texture analysis and classification | |
dc.type | Artículos de revistas | |