Texture is an important property used in classifying the regions of interests in an image. Literally, it is defined as the uniformity of a substance or a surface. Technically, it gives us the information about the spatial arrangement of structures in an image. One of the earliest methods used for texture feature extraction is the Gray-Level Co-occurrence Matrix (GLCM) which contains second orde...

This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with ...

Gray level co-occurrence matrix (GLCM) is an important method to extract the image texture features of synthetic aperture radar (SAR). However, GLCM can only extract the textures under single scale and single direction. A kind of texture feature extraction method combining nonsubsampled contour transformation (NSCT) and GLCM is proposed, so as to achieve the extraction of texture features under...

In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for cla...

The paper describes a new method to segment ischemic stroke region on computed tomography (CT) images by utilizing joint features from mean, standard deviation, histogram, and gray level co-occurrence matrix methods. Presented unsupervised segmentation technique shows ability to segment ischemic stroke region.

This paper describes that actomyosin complex particles are automatically selected. We propose a new approach, which combines both gray level co-occurrence matrix to extract texture features and SVM classifier to detect actomyosin complex particles automatically. Experimental results show that detection rate achieves 93.58%, the false positive rate is 3.66%, and the area under the ROC curve (AUC...

The concept of an algorithm developed for the segmentation of a road border from the content of an image produced by TV camera mounted on a moving vehicle is presented. The extraction of a road boundary is an important step in the context of autonomous vehicle guidance. The segmentation algorithm combines statistical texture descriptors and the ones based on gray level co-occurrence matrix.

Texture segmentation is the process of partitioning an image into regions with different textures containing a similar group of pixels. Detecting the discontinuity of the filter's output and their statistical properties help in segmenting and classifying a given image with different texture regions. In this proposed paper, chili x-ray image texture segmentation is performed by using Gabor filte...

This paper presents preliminary results for the classiication of Pap Smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textural features. We outline a method of nuclear segment-ation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modiied form of the GLCM are extracted over several angle and distance measures. Linear Discriminant...

A detection method of the rice milling degree was proposed based on machine vision with gray-gradient co-occurrence matrix. Using an experimental mill machine, different milling degree samples of rice were prepared. The rice kernel image of the different milling degree was get by a machine vision detecting system, then the texture features of the rice image were obtained by using gray-gradient ...