Local binary pattern is one of the most known descriptors, which is used for texture
classification. Although completed local binary pattern is seemingly the most precise variant …

Artificial neural networks, such as the multilayer perceptron (MLP), have been increasingly
employed in various applications. Recently, deep neural networks, specially convolutional …

We present novel rotationally invariant fully multispectral Markovian textural features applied
for the efficient tree bark recognition. These textural features are derived from the novel …

Numerous researchers have used machine vision in recent years to identify and categorize
clouds according to their volume, shape, thickness, height, and coverage. Due to the …

There are several statistical descriptors for feature extraction from texture images. Local
binary pattern is one of the most popular descriptors for revealing the underlying structure of …

Texture analysis has many important applications, including material recognition, face
recognition, object detection, image segmentation. Local feature descriptors were the …

In this work, we present a discriminative and effective local texture descriptor for bark image
classification. The proposed descriptor is based on three factors, namely, pixel, magnitude …

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer
vision. In most applications, LBP histograms are exploited as texture features leading to a …

An efficient bark recognition method based on a novel wide-sense Markov spiral model
textural representation is presented. Unlike the alternative bark recognition methods based …

In recent years, LBP and its variants have led to significant progress in applying texture
methods to different applications. However, this operator tends to produce high dimensional …