Results 241 to 250 of about 16,583,366 (283)

Image Texture Analysis Based on Gray Level Co-Occurrence Matrix

2023 13th International Conference on Information Technology in Medicine and Education (ITME), 2023
Chen Meilong, Yu Xiehua, Su Shaojun, Lin Daqing   +3 more
exaly   +2 more sources

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications, 2017
Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis
Maktabdar Oghaz, M., Maarof, M. A., Rohani, M. F., Zainal, A., Shaid, S. Z. M.   +4 more
openaire   +2 more sources

Weld Classification Using Gray Level Co-Occurrence Matrix and Local Binary Patterns

2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018
This paper presents an algorithm that can classify weld seams from images, exploiting machine learning techniques. Manual visual inspection is the primary way of evaluating weld seams, in cases where the primary goal is to keep inspection costs low. Such, visual inspections entail manual interpretation and evaluation, which are both time consuming and ...
Philip Valentin, Tsampikos Kounalakis, Lazaros Nalpantidis   +2 more
openaire   +2 more sources

A Supervised Method for Determining Displacement of Gray Level Co-Occurrence Matrix

2011 7th Iranian Conference on Machine Vision and Image Processing, 2011
Gray Level Co-occurrence Matrix (GLCM) is one of the most powerful methods for extracting texture information. GLCM extracts occurrence probability of intensities with a specific displacement between pair pixels. Most applications of GLCM use a single displacement or a distinct number of displacements.
Hassan Nikoo, Hossein Talebi, Abdolreza Mirzaei   +2 more
openaire   +1 more source

Interpolation-Based Gray-Level Co-Occurrence Matrix Computation for Texture Directionality Estimation

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2018
A novel interpolation-based model for the computation of the Gray Level Co-occurrence Matrix (GLCM) is presented. The model enables GLCM computation for any real-valued angles and offsets, as opposed to the traditional, lattice-based model. A texture directionality estimation algorithm is defined using the GLCM-derived correlation feature.
Marcin Kociolek, Peter Bajcsy, Mary Brady, Antonio Cardone   +3 more
openaire   +2 more sources

Gray-level co-occurrence matrix analysis of chromatin architecture in periportal and perivenous hepatocytes

Histochemistry and Cell Biology, 2018
Periportal hepatocytes (PPHs) and perivenous hepatocytes (PVHs) in standard optical microscopy appear to be morphologically identical. However, the functional properties of these two cell populations and their roles in liver lobules are not the same.
Jovana Paunovic, Danijela Vucevic, Tatjana Radosavljevic, Senka Pantic, Milena Veskovic, Igor Pantic   +5 more
openaire   +2 more sources

JPEG image tampering localization based on normalized gray level co-occurrence matrix

Multimedia Tools and Applications, 2018
To locate the tampered region of double compressed JPEG images, one of the most effective methods is based on the statistical characteristic of the images. After tampering operation, the tampered region and the original region will have different statistical distributions. And according to this cue, the histogram of DCT coefficients can be moded as the
Fei Xue, Wei Lu 0001, Ziyi Ye, Hongmei Liu 0001   +3 more
openaire   +2 more sources

Electroencephalography-Based Emotion Recognition Using Gray-Level Co-occurrence Matrix Features

2016
Emotions are very essential for our day-to-day activities such as communication, decision-making and learning. Electroencephalography (EEG) is a non-invasive method to record electrical activity of the brain. To make Human–Machine Interaction (HMI) more natural, human emotion recognition is important.
Narendra Jadhav, Ramchandra Manthalkar, Yashwant V. Joshi   +2 more
openaire   +2 more sources

Improvements on the Gray Level Co-occurrence Matrix Technique to Compute Ischemic Stroke Volume

2002
The purpose of this work was to apply and test Haralick’s gray level co-occurrence matrix (GLCM) technique for automatic calculation and segmentation of the ischemic stroke volume from CT images. For this task, the 3-nearest neighbors classifier was trained to perform stroke and non-stroke area classification.
Andrius Usinskas, Bernd Tomandl, Peter Hastreiter, Klaus Spinnler, Thomas Wittenberg   +4 more
openaire   +2 more sources

An Mean Shift Based Gray Level Co-occurrence Matrix for Endoscope Image Diagnosis

2010
Endoscope is important for detecting gastric lesions. Computer aided analysis of endoscope images is helpful to improve the accuracy of endoscope tests. In this paper, Mean Shift-Gray Level Co-occurrence Matrix algorithm (MS-GLCM), an improved algorithm for computing Gray Level Co-occurrence Matrix (GLCM) based on Mean Shift, is presented to solve the ...
Yilun Wu, Kai Sun, Xiaolin Lin, Shidan Cheng 0001, Su Zhang 0001   +4 more
openaire   +2 more sources