In texture classification, local binary pattern (LBP) is currently one of the most widely-
concerned feature encoding models. Most existing LBP-based texture classification methods …

Texture recognition is an important task in computer vision and, as most problems in the
area nowadays, has benefited from the use of deep convolutional neural networks …

This work proposes a method for texture classification based on the successive application
of a local transform presented here for the first time. Such transform comprises two steps:(1) …

Unsupervised change detection of land cover in multispectral satellite remote sensing
images with a spatial resolution of 2–5 m has always been a challenging task. This paper …

Texture is a visual attribute largely used in many problems of image analysis. Many methods
that use learning techniques have been proposed for texture discrimination, achieving …

In this work, we propose a methodology for texture recognition. The combination of deep
learning with texture encoding techniques has demonstrated to be a powerful strategy to …

Roughness was one of the most visual manifestations of the surface quality of metal parts. It
affected the performance and life of the parts. Accurate and efficient roughness grade …

Although many learning-based approaches have been proposed for texture analysis
showing promising results, they use large and complex architectures and suffer from limited …

As digital images continue to generate an increasing amount of data, image feature
extraction has become a crucial component of image recognition. This paper proposes an …

Background: Metal surface roughness detection is an essential step of quality control in the
metal processing industry. Due to the high manual involvement and poor efficiency of …