In this paper, we propose a simple, efficient, yet robust multiresolution approach to texture
classification-binary rotation invariant and noise tolerant (BRINT). The proposed approach is …

The local binary pattern (LBP) descriptor is widely used in texture analysis because of its
computational simplicity and robustness to illumination changes. However, LBP has …

This paper presents a novel approach for texture classification, generalizing the well-known
local binary pattern (LBP) approach. In the proposed approach, two different and …

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation
invariant texture classification. The proposed LBC can extract the local binary grayscale …

Original Local Binary Pattern (LBP) descriptor has two obvious demerits, ie, it is sensitive to
noise, and sometimes it tends to characterize different structural patterns with the same …

Local binary pattern (LBP) and its variants have been successfully applied in texture feature
extraction. However, it is hard for most LBP-based methods to effectively describe and …

Local binary patterns (LBP) are considered among the most computationally efficient high-
performance texture features. However, the LBP method is very sensitive to image noise and …

Local binary pattern (LBP) has been successfully used in computer vision and pattern
recognition applications, such as biomedical image analysis, remote sensing and image …

The local binary pattern (LBP) operator is a very effective multi-resolution texture descriptor
that can be applied in many image processing applications. However, existing LBP …

This paper presents a novel, simple, yet robust texture descriptor against noise named the
adjacent evaluation local binary patterns (AELBP) for texture classification. In the proposed …