dc.creator | Montoya-Zegarra, JA | |
dc.creator | Papa, JP | |
dc.creator | Leite, NJ | |
dc.creator | Torres, RDS | |
dc.creator | Falcao, AX | |
dc.date | 2008 | |
dc.date | 2014-11-18T03:39:57Z | |
dc.date | 2015-11-26T17:45:57Z | |
dc.date | 2014-11-18T03:39:57Z | |
dc.date | 2015-11-26T17:45:57Z | |
dc.date.accessioned | 2018-03-29T00:28:24Z | |
dc.date.available | 2018-03-29T00:28:24Z | |
dc.identifier | Eurasip Journal On Advances In Signal Processing. Springer, 2008. | |
dc.identifier | 1687-6172 | |
dc.identifier | WOS:000256728300001 | |
dc.identifier | 10.1155/2008/691924 | |
dc.identifier | http://www.repositorio.unicamp.br/jspui/handle/REPOSIP/80822 | |
dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/80822 | |
dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/80822 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1288344 | |
dc.description | Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system. Copyright (c) 2008 Javier A. Montoya-Zegarra et al. | |
dc.language | en | |
dc.publisher | Springer | |
dc.publisher | New York | |
dc.publisher | EUA | |
dc.relation | Eurasip Journal On Advances In Signal Processing | |
dc.relation | EURASIP J. Adv. Signal Process. | |
dc.rights | aberto | |
dc.rights | http://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0 | |
dc.source | Web of Science | |
dc.subject | Fuzzy Connectedness | |
dc.subject | Classification | |
dc.subject | Segmentation | |
dc.subject | Retrieval | |
dc.subject | Filters | |
dc.subject | Models | |
dc.subject | Algorithms | |
dc.title | Learning how to extract rotation-invariant and scale-invariant features from texture images | |
dc.type | Artículos de revistas | |