Texture analysis of non-uniform images using GLCM

Saroja, G. Arockia Selva; Sulochana, C. Helen

doi:10.1109/cict.2013.6558306

Cited by 15 publications

(4 citation statements)

References 13 publications

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“…Textures are characteristic intensity variations that originate from the roughness of an object surface. The texture of an image is classified into first-order, second-order, and higher-order statistics [57]. There are a variety of methods of extracting texture features including Local Binary Patterns (LBP), the Canny edge detection, discrete wavelet transform, and gray level occurrence matrix, among others [58][59][60].…”

Section: Texture Feature Extractionmentioning

confidence: 99%

An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

Kimutai

Ngenzi

Said

et al. 2020

Data

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Tea is one of the most popular beverages in the world, and its processing involves a number of steps which includes fermentation. Tea fermentation is the most important step in determining the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the following methods: monitoring change in color of tea as fermentation progresses and tasting and smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently, they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). TeaNet was more superior in the classification tasks compared to the other machine learning techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation dataset that is available for use by the community.

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Section: Texture Feature Extractionmentioning

confidence: 99%

An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

Kimutai

Ngenzi

Said

et al. 2020

Data

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“…Metode Gray Level Co-occurence Matrix (GLCM) adalah salah satu metode yang membangkitkan fitur berdasarkan tekstur dari suatu citra. Salah satu kelebihan GLCM adalah tidak terpengaruh oleh illuminasi citra yang tidak merata [7]. GLCM bekerja dengan mendeteksi posisi dari piksel yang mempunyai kesamaan level keabuan.…”

Section: Pendahuluanunclassified

Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM

Bahri

Saddami

Arnia

et al. 2019

JNTE

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Pengenalan wajah merupakan salah satu bagian dari penelitian biometrika. Pengenalan wajah banyak digunakan dalam proses identifikasi manusia. Metode ekstraksi fitur Speed-Up Robust Feature (SURF) merupakan salah satu metode yang digunakan untuk mengenali wajah. Penelitian ini bertujuan untuk membandingkan kinerja sistem pengenalan wajah dengan menggunakan metode ekstraksi fitur SURF dan Gray Level Co-occurence Matrix (GLCM). Pada penelitian ini, data input wajah akan diekstraksi fiturnya menggunakan SURF dan GLCM. Setiap fitur digunakan pada tahapan klasifikasi menggunakan Support Vector Machine (SVM). Data yang digunakan merupakan data yang didapatkan dari National Cheng Kung University (NCKU). Data wajah NCKU mempunyai sudut rotasi yang lebih banyak. Dataset yang digunakan pada penelitian ini terdiri dari 10 kelas yang menunjukkan 10 subjek penelitian. Pengenalan wajah menggunakan metode SURF dan SVM mempunyai akurasi 85%, sedangkan menggunakan metode GLCM mempunyai akurasi 50%. Hasil menunjukkan bahwa metode SURF mempunyai kinerja yang lebih baik dari metode GLCM.

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“…34 However, limited by image rotation and external illumination variations, the performance of single texture extraction methods was often unsatisfactory. 35 LBP could capture local details, but it was easy to lose global information. 36 GLCM was less accurate in the region near the class boundary.…”

Section: Introductionmentioning

confidence: 99%

Roughness detection method based on image multi-features

Pan

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

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Texture analysis of non-uniform images using GLCM

Cited by 15 publications

References 13 publications

An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM

Roughness detection method based on image multi-features

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