There have been several approaches to the classification of texture in images. Most approaches will take certain local attributes or features into account and base the classification on these measures. In here we demonstrate the use of a statistical approach to separate the structure and texture background present in images. Modelling is based on normal images which only contain a texture backg...

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil textur...

In this paper, a texture classification method based on multi-model feature integration by Bayesian networks is proposed. Considering that many image textures exhibit both structural and statistical properties, two feature sets based on two texture models––the Gabor model and the Gaussian Markov random field model are used to describe the image properties in both structure and statistics. A Bay...

The project presents a memory and run-time efficient image texture classification. The project implements the best available algorithms for texture classification on a NVIDIA GPU to exploit the possible parallelism in the algorithms to achieve considerable speed up. I present a near real time implementation of texture classification. The method proposed by Tuzel et al. is used for the feature e...

This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to onedimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. F...

Textures have an intrinsic multiresolution property due to their varying texel size. This suggests using multiresolution techniques in texture analysis. Recently linear scale space techniques along with multiple classifier systems have been proposed as an effective approach in texture classification especially at small sample sizes. However, linear scale space blurs and dislocates conceptually ...

Texture analysis is a highly significant area in the arena of computer vision and connected pitches. Not the least, classification is also equally important and laudable zone in the area of understanding the texture pattern and is gaining a lot of interest among the researchers in the field of computer vision. It finds a widespread application in area of pattern classification, robotic applicat...

We present a new method for texture image classification using Basic Gray Level Aura Matrices (BGLAMs). Given an unseen texture image, our approach classifies it into one of the pre-learned classes, each of which is characterized using BGLAMs. There are two stages in our algorithm: a learning stage and a classification stage. In the first stage, models of texture classes are learned from the BG...

Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of...

Computerized processing of medical images can ease the search of the representative features in the images. The endoscopic images possess rich information expressed by texture. Regions affected by diseases, such as ulcer or coli, may have different texture features. The texture model implemented in this study is Local Binary Pattern (LBP) and a loglikelihood-ratio, called the G-statistic, is us...