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 res- olution 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.
LO PRESTI, L., LA CASCIA, M. (2011). Entropy-based Localization of Textured Regions. In Image ANalysis and Processing - ICIAP 2011 (pp.616-625) [DOI 10.1007/978-3-642-24085-0_63].
Entropy-based Localization of Textured Regions
LO PRESTI, Liliana;LA CASCIA, Marco
2011-01-01
Abstract
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 res- olution 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.File | Dimensione | Formato | |
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