Relevant bibliographies by topics / Grey Level Co-Occurrence Matrix (GLCM)

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  2. Dissertations / Theses
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Academic literature on the topic 'Grey Level Co-Occurrence Matrix (GLCM)'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Grey Level Co-Occurrence Matrix (GLCM)"

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Toshikj, Emilija, and Bojan Prangoski. "Grey level co-occurrence matrix (GLCM) for textile print analysis." Tekstilna industrija 70, no. 4 (2022): 34–40. http://dx.doi.org/10.5937/tekstind2204034t.

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Print mottle is a print defect. This print defect has great attention in print quality assessment. Print mottle is determined by the grey level co-occurrence matrix (GLCM). An important parameter in the GLCM processing is the direction angle of pixels in the digitalized print image. This research aimed to investigate the influence of the direction angle, which is an important input parameter in GLCM processing, on the output parameters, such as entropy, energy, contrast, correlation, and homogeneity. Hence, prints were generated in four different colors (cyan, magenta, yellow and black) on white polyester elastase fabric by sublimation printing. The non-uniformity of the print for each color was processed at different direction angles, such as 0° (horizontal), 90° (vertical), 45° (right-diagonal), and 135° (left-diagonal). Values for GLCM parameters obtained at different direction angles were slightly different regardless of print color. The choice of direction angle influenced the values of GLCM parameters. The average of all four directional angle values obtained for each GLCM parameter was taken. The GLCM processing method can be used for prints of different colors, patterns, and different quality levels to evaluate their print uniformity.
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Datta, Anurup, Samik Dutta, Surjya K. Pal, Ranjan Sen, and Sudipta Mukhopadhyay. "Texture Analysis of Turned Surface Images Using Grey Level Co-Occurrence Technique." Advanced Materials Research 365 (October 2011): 38–43. http://dx.doi.org/10.4028/www.scientific.net/amr.365.38.

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The main purpose of this work was to study the applicability of an image texture analysis method, namely, the grey level co-occurrence matrix (GLCM) method for the examination of the smoothness of the images of a turned surface. The effect of the variation of the pixel pair spacing (pps) on the construction of the GLCM was also considered and then, contrast and homogeneity were calculated from the GLCMs which served as texture descriptors for the quality of the machined surface. Finally, the variation of these texture descriptors with cutting time was analyzed and compared with the variation of tool wear and surface roughness with cutting time.
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WALKER, ROSS F., PAUL T. JACKWAY, and DENNIS LONGSTAFF. "GENETIC ALGORITHM OPTIMIZATION OF ADAPTIVE MULTI-SCALE GLCM FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 01 (2003): 17–39. http://dx.doi.org/10.1142/s0218001403002228.

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We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification "worth" is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.
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Rahman, Muhammad Saidi. "KLASIFIKASI MOTIF SASIRANGAN BERBASIS FITUR GREY LEVEL CO-OCCURRENCE MATRIES MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK." Technologia: Jurnal Ilmiah 9, no. 4 (2018): 250. http://dx.doi.org/10.31602/tji.v9i4.1540.

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Sasirangan adalah kain khas Kalimantan selatan yang dibuat dengan teknik jerujuk. Sasirangan memiliki banyak motif yang berjumlah sekitar 20 motif. Pada penelitian ini motif yang digunakan untuk klasifikasi sasirangan ada 3 motif yaitu Abstrak, Kulat Kurikit dan Hiris Gegatas dengan jumlah citra yang digunakan pada penitilian ini setiap motif ada 10 citra. Grey Level Co-occurrence Matrix (GLCM) digunakan untuk ekstrasi fitur pada gambar sasirangan. Dengan mengambil nilai 5 besaran dari GLCM yaitu Entropi, Korelasi, Kontras, Angular Second Moment (ASM) dan Inverse Different Moment (IDM) dari 4 sudut citra yang berbeda yaitu 00, 450, 900 dan 1350. Selanjutnya hasil ekstrasi akan di klasifikasikan dengan menggunakan metode Backpropagation Neural Network dengan beberapa skenario pengujian melalui X-Validation. Tipe validasi yang diuji yaitu Stratified Sampling, Linear Sampling dan Shuffled Sampling dengan ketentuan Number Validation 2 sampai 10. Hasil akurasi tertinggi pada number validation 10 dengan akurasi 95% pada ketiga tipa validasi. Keywords : Sasirangan, Grey Level Co-occurrence Matrix(GLCM), Backpropagation Neural Network.
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Nguyen, Hoang Duc, Thuong Tien Le, Tuan Hong Do, and Cao Thu Bui. "A NEW DESCRIPTOR FOR IMAGE RETRIEVAL USING CONTOURLET COOCCURRENCE." Science and Technology Development Journal 15, no. 2 (2012): 5–16. http://dx.doi.org/10.32508/stdj.v15i2.1785.

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In this paper, a new descriptor for the feature extraction of images in the image database is presented. The new descriptor called Contourlet Co-Occurrence is based on a combination of contourlet transform and Grey Level Co-occurrence Matrix (GLCM). In order to evaluate the proposed descriptor, we perform the comparative analysis of existing methods such as Contourlet [2], GLCM [14] descriptors with Contourlet Co-Occurrence descriptor for image retrieval. Experimental results demonstrate that the proposed method shows a slight improvement in the retrieval effectiveness.
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Naibaho, Rizky Fauzan, and Indah Purnama Sari. "Implementasi Metode Gray Level Co-Occurrence Matrix Menganalisis Tekstur Kulit Wajah." sudo Jurnal Teknik Informatika 3, no. 4 (2025): 172–82. https://doi.org/10.56211/sudo.v3i4.668.

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GLCM merupakan metode analisis gambar yang dapat mengukur berbagai fitur tekstur seperti kontras, korelasi, energi, dan homogenitas dari gambar bergradasi abu-abu. Skripsi ini bertujuan untuk mengeksplorasi efektivitas GLCM dalam mengekstraksi dan menganalisis informasi tekstur dari kulit wajah, serta aplikasinya dalam bidang seperti deteksi penyakit kulit dan pengenalan wajah. dalam penelitian ini, gambar kulit wajah dikumpulkan dan diproses menggunakan teknik GLCM untuk menghasilkan matriks co-occurrence pada berbagai jarak dan sudut. Fitur-fitur tekstur yang dihasilkan dari GLCM kemudian dianalisis untuk menentukan pola-pola tekstur khas yang dapat dikaitkan dengan kondisi kulit atau identitas individu. Hasil penelitian menunjukkan bahwa GLCM dapat memberikan informasi tekstur yang signifikan dan relevan dalam konteks analisis kulit wajah, dengan potensi aplikasi dalam teknologi kesehatan dan keamanan.
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Singh, Amarpreet, and Sanjogdeep Singh. "Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries." Journal of Image and Graphics 11, no. 1 (2023): 82–90. http://dx.doi.org/10.18178/joig.11.1.82-90.

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With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.
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Dhanalakshmi, P., and Dr G. Satyavathy. "Grey Level Co-Occurrence Matrix (GLCM) and Multi-Scale Non-Negative Sparse Coding For Classification of Medical Images." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 481–93. http://dx.doi.org/10.5373/jardcs/v11sp10/20192835.

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Chen, Chaoyue, Hongyu Zhuo, Xiawei Wei, and Xuelei Ma. "Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy." Contrast Media & Molecular Imaging 2019 (November 12, 2019): 1–7. http://dx.doi.org/10.1155/2019/5481491.

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Introduction. The purpose of this study was to evaluate the prognostic value of texture features on contrast-enhanced magnetic resonance imaging (MRI) for patients with primary central nervous system lymphoma (PCNSL). Methods. In this retrospective study, fifty-two patients diagnosed with PCNSL were enrolled from October 2010 to March 2017. The texture feature of tumor tissue on the histogram-based matrix (histo-) and the grey-level co-occurrence matrix (GLCM) was retrieved by contrast-enhanced T1-weighted imaging before any antitumor treatment. Receiver operating characteristic curve analyses were performed to obtain their optimal cutoff values, based on which we dichotomized patients into subgroups. The Kaplan–Meier analyses were conducted to compare overall survival (OS) of subgroups, and multivariate Cox regression analyses were used to determine if they could be taken as independent prognostic factors. Results. Ten texture features were extracted from the MR image, including Energy, Entropy, Kurtosis, Skewness on the histogram-based matrix, and Correlation, Contrast, Dissimilarity, Energy, Entropy, and Homogeneity on the grey-level co-occurrence matrix. Three of them (GLCM-Contrast, GLCM-Dissimilarity, and GLCM-Homogeneity) are shown to be significant in relation to overall survival (OS). The multivariate Cox regression analyses suggest that GLCM-Homogeneity could be taken as independent predictors. Conclusions. The texture features of contrast-enhanced magnetic resonance imaging (MRI) could potentially serve as prognostic biomarkers for PCNSL patients.
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Wang, Hui, Xiang Wei Kong, and Han Wang. "Research on Building Method of Gray Level Co-Occurrence Matrix Suitable to Natural Texture." Advanced Materials Research 1079-1080 (December 2014): 432–35. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.432.

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CIn order to build gray level co-occurrence matrix suitable to natural texture, a method based on separable criterion was proposed. Combined correlation matrix of feature parameters with character of natural texture, 5 independent feature parameters are extracted from 11 feature parameters of gray level co-occurrence matrix (GLCM). Building factors of GLCM, which is appropriate to describe wood texture, are confirmed by using the separable criterion, when d equals to 2 and g equals to 16.
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Dissertations / Theses on the topic "Grey Level Co-Occurrence Matrix (GLCM)"

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Gadkari, Dhanashree. "IMAGE QUALITY ANALYSIS USING GLCM." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3246.

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Gray level co-occurrence matrix has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the gray level co-occurrence matrix help understand the details about the overall image content. The aim of this research is to investigate the use of the gray level co-occurrence matrix technique as an absolute image quality metric. The underlying hypothesis is that image quality can be determined by a comparative process in which a sequence of images is compared to each other to determine the point of diminishing returns. An attempt is made to study whether the curve of image textural features versus image memory sizes can be used to decide the optimal image size. The approach used digitized images that were stored at several levels of compression. GLCM proves to be a good discriminator in studying different images however no such claim can be made for image quality. Hence the search for the best image quality metric continues.<br>M.S.<br>Other<br>Arts and Sciences<br>Modeling and Simulation
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Tirumazhisai, Manivannan Karpagam. "Development of Gray Level Co-occurrence Matrix based Support Vector Machines for Particulate Matter Characterization." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341577486.

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Ferguson, Jeremiah R. "Using the grey-level co-occurrence matrix to segment and classify radar imagery." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447631.

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Leigh, Steve. "Automated Ice-Water Classification using Dual Polarization SAR Imagery." Thesis, 2013. http://hdl.handle.net/10012/7706.

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Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use.
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Μουστάκα, Μαρία. "Ταξινόμηση δεδομένων ραντάρ συνθετικού ανοίγματος (SAR) με χρήση νευρωνικών δικτύων". Thesis, 2013. http://hdl.handle.net/10889/7247.

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Η χρήση των δεδομένων Ραντάρ Συνθετικού Ανοίγματος (SAR) σε εφαρμογές απομακρυσμένης παρακολούθησης της Γης έχει ήδη αρχίσει να πρωταγωνιστεί τις τελευταίες δεκαετίες. Τα συστήματα SAR με δυνατότητες μεταξύ άλλων συνεχούς λειτουργίας παντός καιρού, ημέρα και νύχτα, προσφέροντας μεγάλη κάλυψη εδάφους και με δυνατότητα λήψης απεικονίσεων πολλαπλών πολώσεων, έχουν αποτελέσει πηγή πολύτιμων πληροφοριών τηλεπισκόπησης. Έτσι, η χρήση των SAR δεδομένων για την ταξινόμηση κάλυψης γης προσελκύει όλο και περισσότερο την προσοχή των ερευνητών και φαίνεται να είναι πολλά υποσχόμενη. Η παρούσα ειδική επιστημονική εργασία έχει στόχο τη μελέτη και ερμηνεία των δεδομένων SAR μέσω επιβλεπόμενης ταξινόμησης, με τη χρήση νευρωνικών δικτύων (Neural Networks). Αφού πρώτα γίνεται εκτενής αναφορά στη τεχνολογία και τα συστήματα SAR, παρουσιάζεται αναλυτικά η πειραματική διαδικασία ταξινόμησης τριών βασικών δομών κάλυψης γης. Τα δεδομένα προέρχονται από το Προηγμένο Ραντάρ Συνθετικού Ανοίγματος (ASAR) του δορυφόρου ENVISAT από τον Ευρωπαϊκό Οργανισμό Διαστήματος και αφορούν στην ευρύτερη περιοχή του Άμστερνταμ. Πριν την διεξαγωγή της ταξινόμησης, τα δεδομένα δέχθηκαν τις απαραίτητες διαδικασίες προ-επεξεργασίας (ραδιομετρική βαθμονόμηση, γεωαναφορά, φιλτράρισμα θορύβου, συμπροσαρμογή). Όσον αφορά τη διαδικασία της ταξινόμησης, εξετάζεται η συμπεριφορά του ταξινομητή του νευρωνικού δικτύου για μεταβολές ποικίλων παραμέτρων, όπως η επιλογή δεδομένων διαφόρων πολώσεων, το πλήθος των νευρώνων κ.α. και ήδη από τα πρώτα πειράματα λαμβάνονται ικανοποιητικά αποτελέσματα. Στη συνέχεια εφαρμόζονται τεχνικές σύνθεσης πληροφορίας (average rule, majority rule) βελτιώνοντας τις επιδόσεις ταξινόμησης. Τέλος, ένα σημαντικό βήμα που εφαρμόζεται στη διαδικασία ταξινόμησης αποτελεί η εξαγωγή χαρακτηριστικών υφής από τις μήτρες συνεμφάνισης φωτεινοτήτων (Gray Level Co-occurrence Matrix-GLCM) και μήκους διαδρομής φωτεινότητας (Gray Level Run Length Matrix-GLRLM). Η χρήση των χαρακτηριστικών αυτών βελτιστοποιεί το σύστημα ταξινόμησης, δίνοντας εξαιρετικά αποτελέσματα.<br>The use of Synthetic Aperture Radar (SAR) data in remote sensing applications has become a cutting edge technology during the past few decades. The SAR systems have several capabilities, like day & night and all weather operation and they offer large ground coverage with the ability of multi-polarized imagery; therefore, they have proved to be a valuable source of remote sensing data. As a result, the use of SAR data for land cover classification increasingly attracts the attention of researchers and seems to be highly promising. Goal of this master thesis is the study and interpretation of SAR data through supervised classification, with the use of Neural Networks method. First, there is an extensive presentation of SAR systems and technology and then follows the detailed presentation of the experimental classification process for three basic land cover structures. The available data are from the Advanced SAR (ASAR) radar of the ESA ENVISAT satellite and correspond to the Amsterdam city and suburbs. Prior to the classification process, the data have been appropriately pre-processed (radiometric calibration, geocoding, speckle filtering, co-registration). Regarding the classification process, the response of the neural network classifier with the variation of several parameters (e.g. data polarization and number of neurons) is studied and from the initial test already the results were quite satisfactory. Further on, ensemble classifying methods (average rule, majority rule) are applied to improve the classification performance. Finally, as an essential step applied in the classification process is the textural feature extraction from Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). The use of these texture features optimizes the classification system, resulting to an exceptional performance.
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Tung, Do Thanh, and 杜青松. "Assessment of the Grey-Level Co-occurrence Matrix for Land Use/Land Cover Classification using Multi-spectral UAV Image." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/9mw9xe.

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碩士<br>逢甲大學<br>都市計畫與空間資訊學系<br>104<br>The application of UAV has been popular in recent years due to their advantages. The UAV systems are more advantageous as compared with other manned aircraft systems. The main advantages of UAV systems are that they can fly in high risk location, unreachable areas, and at very low altitude close to the objects without threatening human life. Moreover, the UAV images also can exhibit ground surface characteristics in very high spatial resolution. Thus, the level of detail present in the UAV image has increased considerably when compared to the other multispectral satellite images and aerial photos. In this research, multispectral UAV images have been used to extract texture features for land cover/land use classification. Five cover types have been classified based on textural/spectral combination. The texture estimation is normally based on the grey-level co-occurrence matrix (GLCM) method. The texture features are extracted from UAV near infrared band by using four textural parameters (ASM, CON, ENT and VAR), fifteen window sizes (from 3x3 to 59x59) and two quantization levels (16 and 32). The supervised maximum likelihood algorithm is selected to apply to the four UAV spectral bands combined with each textural parameter independently, and to the four spectral bands combined with four textural parameters. The classification accuracy is measured by kappa coefficient calculated from confusion matrices. The main aim of this research is to evaluate the possibility of using texture features extracted by GLCM as additional information for UAV images to tackle the problem in relation to the increased internal spectral-radiometric variation of land cover types and spectral resolution limitation of UAV images. The research results show that the classification accuracy is significantly improved when the texture features are added to the UAV spectral images. The improvement of classification accuracy appeared to be different by different texture features of each cover type. The results of this research are essential for evaluating which texture feature is useful for detail and accurate land use/land cover classification.
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Book chapters on the topic "Grey Level Co-Occurrence Matrix (GLCM)"

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Kamdar, Aayush, Vihaan Sharma, Sagar Sonawane, and Nikita Patil. "Lung Cancer Detection by Classifying CT Scan Images Using Grey Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbours." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0475-2_27.

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Karimah, Fathin Ulfah, and Agus Harjoko. "Classification of Batik Kain Besurek Image Using Speed Up Robust Features (SURF) and Gray Level Co-occurrence Matrix (GLCM)." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7242-0_7.

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Rachmad, Aeri, Rinci Kembang Hapsari, Wahyudi Setiawan, Tutuk Indriyani, Eka Mala Sari Rochman, and Budi Dwi Satoto. "Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-174-6_4.

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Priyanka, V., and V. Uma Maheswari. "Automated Glaucoma Detection Using Cup to Disk Ratio and Grey Level Co-occurrence Matrix." In Machine Learning and Information Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2_42.

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Tai, Shen-Chuan, Zih-Siou Chen, Wei-Ting Tsai, Chin-Peng Lin, and Li-li Cheng. "A Mass Detection System in Mammograms Using Grey Level Co-occurrence Matrix and Optical Density Features." In Advances in Intelligent Systems and Applications - Volume 2. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35473-1_37.

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Abdulla, Beshaier Ali, Yossra Hussian Ali, and Nuha Jameel Ibrahim. "Similar Image Retrieval Based on Grey-Level Co-Occurrence Matrix and Hu Invariants Moments Using Parallel Computing." In Research in Intelligent and Computing in Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7527-3_14.

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Sinha, Anupa, and Pooja Sharma. "Using Grey Level Co-occurrence Matrix Method to Extract Minute Fingerprint Features Improves FAR and FRR for Fingerprint." In Recent Developments in Microbiology, Biotechnology and Pharmaceutical Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003618140-49.

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Reddy, Chappidi Sree Teja, and Geetha Ramalingam. "Examining and Comparing the Precision of Brain Tumor Identification Using the Grey Level Co-Occurrence Matrix and Berkeley Wavelet Transform." In Case Studies on Holistic Medical Interventions. CRC Press, 2024. https://doi.org/10.1201/9781003596684-19.

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Choudhury, Dilip k., and Sujata Dash. "Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network." In Advances in Computational Intelligence and Robotics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2857-9.ch014.

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The class of Textiles produced from terephthalic acid and ethylene glycol by condensation polymerization has many end-uses for example these are used as filter fabric in railway track to prevent soil erosion, in cement industry these are used in boiler department as filter fabric to prevent the fly-ash from mixing in the atmosphere. Presently, the quality checking is done by the human in the naked eye. The automation of quality check of the non-Newtonian fabric can be termed as Image Analysis or texture analysis problem. A Simulation study was carried out by the process of Image Analysis which consists of two steps the former is feature extraction and the later part is recognition. Various techniques or tools that are presently studied in research for texture feature extraction are Grey level co-occurrence matrix(GLCM), Markov Random Field, Gabor filter. A GLCM matrix with 28 Haralick features were taken as input for this chapter.
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Babu, C. V. Suresh, Ambati Swapna, Dama Swathi Chowdary, Burri Sujit Vardhan, and Mohd Imran. "Leaf Disease Detection Using Machine Learning (ML)." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9231-4.ch010.

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This method's central idea is the generation of features using grey level co-occurrence matrices (GLCM). The spatial interactions between pixels are to be measured by the matrices. A grey-level co-occurrence matrix is used to extract co-occurrence features. Texture classification can be used for a number of applications, such as pattern identification, object tracking, and shape recognition, when done correctly and accurately. The images of the leaves are used to identify plant diseases. As a result, it is beneficial to apply image processing techniques to identify and categorise illnesses in agricultural applications. Making predictions or judgements without being explicitly programmed is possible by utilising machine learning algorithms to create a model based on test data, also referred to as “training data.” Because it is very challenging for humans to detect disease in leaves, we have introduced a classification of plant leaf diseases in this project. This study extracts the leaf's textural properties and compares them to classifiers that use RF, LDA, NN, and CNN.
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Conference papers on the topic "Grey Level Co-Occurrence Matrix (GLCM)"

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Gao, Zixin, Yongan Zhang, Pei Liu, Ruijin Fu, and Bing Zhang. "Auto-Focusing in Off-axis Digital Holography Using Gray Level Co-Occurrence Matrix." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jd4a.88.

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We propose employing the gray level co-occurrence matrix (GLCM) to compute the contrast characteristics of holographic reconstructions for auto-focusing. Simulation results demonstrate that this method represents an effective approach for off-axis digital holographic auto-focusing.
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Roodbary, Amin Aghatabar, Mohammad Hassan Bastani, and Fereidoon Behnia. "Classification of automotive radar targets using Gray Level Co-occurrence Matrix(GLCM)." In 2024 32nd International Conference on Electrical Engineering (ICEE). IEEE, 2024. http://dx.doi.org/10.1109/icee63041.2024.10667870.

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Arikaran, N., G. Prabu, Arya Ejoumalai, A. Bhuvanesh, S. Kamalesh, and R. Sathishkumar. "Tomato Leaves Disease Detection Using Gray Level Co-Occurrence Matrix (GLCM) and Image Processing Techniques." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894394.

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Sahu, Mridu, Roopa Golchha, Aditya Prasad, Shreya Pandey, and Sheekha Babar. "Brain Tumor Classification Using Feature Extraction Techniques: A Comparative Study of Local Binary Patterns (LBP) and the Gray Level Co-occurrence Matrix (GLCM) with Random Forest Classification." In 2025 International Conference on Ambient Intelligence in Health Care (ICAIHC). IEEE, 2025. https://doi.org/10.1109/icaihc64101.2025.10957414.

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Wahyuni, Ayutri, Zahir Zainuddin, and Ingrid Nurtanio. "Classification of Botulinum Toxin Dosage for Upper Facial Wrinkles Using Inception-V3 Based on Grey Level Co-Occurrence Matrix Feature." In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10729993.

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Winarno, Edy, Wiwien Hadikurniawati, Setyawan Wibisono, and Anindita Septiarini. "Edge Detection and Grey Level Co-Occurrence Matrix (GLCM) Algorithms for Fingerprint Identification." In 2021 2nd International Conference on Innovative and Creative Information Technology (ICITech). IEEE, 2021. http://dx.doi.org/10.1109/icitech50181.2021.9590134.

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Abolghasemi, M., H. Aghainia, K. Faez, and M. A. Mehrabi. "LSB data hiding detection based on gray level co-occurrence matrix (GLCM)." In 2008 International Symposium on Telecommunications (IST). IEEE, 2008. http://dx.doi.org/10.1109/istel.2008.4651382.

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Tan, Jiaxing, Yongfeng Gao, Weiguo Cao, et al. "GLCM-CNN: Gray Level Co-occurrence Matrix based CNN Model for Polyp Diagnosis." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834585.

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Abbas, Zeeshan, Mobeen-ur Rehman, Shahzaib Najam, and S. M. Danish Rizvi. "An Efficient Gray-Level Co-Occurrence Matrix (GLCM) based Approach Towards Classification of Skin Lesion." In 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, 2019. http://dx.doi.org/10.1109/aicai.2019.8701374.

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Pramestya, Ravy Hayu, Dwi Ratna Sulistyaningrum, Budi Setiyono, Imam Mukhlash, and Zaimatul Firdaus. "Road Defect Classification Using Gray Level Co-Occurrence Matrix (GLCM) and Radial Basis Function (RBF)." In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2018. http://dx.doi.org/10.1109/iciteed.2018.8534769.

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