Results 41 to 50 of about 220,110 (307)

Use of a gray level co-occurrence matrix to characterize duplex stainless steel phases microstructure [PDF]

open access: yes, 2013
Duplex stainless steels are widely used in industry. This is due to their higher strength compared to austenitic steels and to their higher toughness than ferritic steels.
Renzetti, F. R., Zortea, L.
core   +2 more sources

Roughness Analysis of Sea Surface From Visible Images by Texture

open access: yesIEEE Access, 2020
This paper presents a roughness analysis of sea surface from visible images by feature measurements of texture for the first time. The algorithms presented in this paper include six texture feature measurements of sea surface use gray level co-occurrence
Hailang Pan   +5 more
doaj   +1 more source

Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy

open access: yesRemote Sensing, 2022
The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions.
Moritz Rösch   +3 more
doaj   +1 more source

Partial Discharge Pattern Recognition of Transformers Based on the Gray-Level Co-Occurrence Matrix of Optimal Parameters

open access: yesIEEE Access, 2021
The partial discharge (PD) is the most common fault of transformers, which is the main factor affecting the stable operation of transformers. Therefore, the PD should be monitored and identified timely to improve the reliability of the transformers.
Shengya Sun   +5 more
semanticscholar   +1 more source

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

open access: yesJOIV: International Journal on Informatics Visualization, 2022
Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background.
Sudi Mariyanto Al Sasongko   +2 more
doaj   +1 more source

Ekstrak Ciri Komunikasi Nonverbal Menggunakan Gray Level Co-Occurrence Matrix

open access: yesI N F O R M A T I K A, 2020
Komunikasi mengandung dua dimensi verbal dan non verbal. Perilaku komunikasi non verbal dievaluasi menggunakan perhitungan ekstrak ciri Gray Level Co-Occurrence Matrix (GLCM). Video pelamar diekstrak untuk mengambil gestur mata, gestur mulut dan gestur kepala.
openaire   +2 more sources

Breast Cancer Histopathological Image Recognition Based on Pyramid Gray Level Co-Occurrence Matrix and Incremental Broad Learning

open access: yesElectronics, 2022
In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model ...
Jia Li, Jingwen Shi, Hexing Su, Lin Gao
semanticscholar   +1 more source

The Implementation of GLCM and ANN Methods to Identify Dragon Fruit Maturity Level

open access: yesIlkom Jurnal Ilmiah, 2023
The identification of the maturity level of dragon fruit in this study was divided into two groups of ripeness: the unripe and the ripe. This study aims to classify the maturity level based on dragon fruit images using the feature extraction method, the ...
Muhammad Faisal   +2 more
doaj   +1 more source

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

open access: yesEAI Endorsed Transactions on e-Learning, 2023
As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images, manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI.
openaire   +1 more source

Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features

open access: yesApplied Sciences, 2021
The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques.
Heechang Lee   +6 more
doaj   +1 more source

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