6533b832fe1ef96bd129a29f

RESEARCH PRODUCT

Entropy-based Localization of Textured Regions

Liliana Lo PrestiMarco La Cascia

subject

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniTexture atlasComputer sciencebusiness.industryLocal binary patternsLow resolutionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCognitive neuroscience of visual object recognitionLatent Dirichlet allocationsymbols.namesakesymbolsEntropy (information theory)SegmentationComputer visionArtificial intelligencebusinessimage analysis textureComputingMethodologies_COMPUTERGRAPHICS

description

Appearance description is a relevant field in computer vision that enables object recognition in domains as re-identification, retrieval and classification. Important cues to describe appearance are colors and textures. However, in real cases, texture detection is challenging due to occlusions and to deformations of the clothing while person's pose changes. Moreover, in some cases, the processed images have a low resolution and methods at the state of the art for texture analysis are not appropriate. In this paper, we deal with the problem of localizing real textures for clothing description purposes, such as stripes and/or complex patterns. Our method uses the entropy of primitive distribution to measure if a texture is present in a region and applies a quad-tree method for texture segmentation. We performed experiments on a publicly available dataset and compared to a method at the state of the art[16]. Our experiments showed our method has satisfactory performance.

yearjournalcountryeditionlanguage
2011-09-01
http://hdl.handle.net/10447/76873