Texture analysis is a classical yet challenging task in computer vision for which deep neural
networks are actively being applied. Most approaches are based on building feature …

Convolutional neural networks have shown successful results in image classification
achieving real-time results superior to the human level. However, texture images still pose …

Texture recognition is one of the most important tasks in computer vision, with numerous
applications in several areas. Despite the recent success of end-to-end deep learning …

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 …

The recognition of road surface conditions significantly influences the active safety control of
autonomous driving vehicles and overall driving performance. Due to the improvement of …

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) …

Texture recognition has predominantly relied on methods based on handcrafted features
and more recently, on Convolutional Neural Network (CNN)-based methods. However …

In the midst of busy society and high lifestyle, there are now many car offerings with
advanced features. The more sophisticated a car is, the price increases. This makes people …

Modeling contextual relationships in images as graph inference is an interesting and
promising research topic. However, existing approaches only perform graph modeling of …

Despite the recent success of convolutional neural networks in computer vision in general,
texture images still pose an important challenge to those models, especially when dealing …