To investigate the effect of image preprocessing, in respect to intensity inhomogeneity
correction and noise filtering, on the robustness and reproducibility of the radiomics features …

Extracting effective features is always a challenging problem for texture classification
because of the uncertainty of scales and the clutter of textural patterns. For texture …

In this work, we present an ensemble of descriptors for the classification of virus images
acquired using transmission electron microscopy. We trained multiple support vector …

In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix
and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP. d The Hessian …

Describing texture is a very challenging problem for many image-based expert and
intelligent systems (eg defective product detection, people re-identification, abnormality …

Pattern recognition and computer vision fields experienced the proposal of several
architectures and approaches to deal with the demands of real world applications including …

To enhance the weakness of Local Binary Pattern (LBP) and its state-of-the-art variants, this
letter presents a new variant of the local concave microstructure pattern (LCvMSP). The …

In this article, a novel pyramid and multi kernel based method is proposed to increased
success of the local binary pattern (LBP). Signum, ternary and quaternary binary feature …

Visual texture classification plays a critical role in computer vision and pattern recognition.
As one of the most popular texture descriptors, local binary pattern (LBP) has achieved …

In this paper, four novel, simple and robust approaches, which are left to right local binary
patterns (LBPL L2R), top to down local binary patterns (LBP T2D), cube surface local binary …