Gotowe bibliografie tematyczne / Grey Level Co-Occurrence Matrix (GLCM)

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  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji

Gotowa bibliografia na temat „Grey Level Co-Occurrence Matrix (GLCM)”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "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 whi
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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 o
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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. Gene
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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
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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 di
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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 fa
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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
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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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Rozprawy doktorskie na temat "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
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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.
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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 επισ
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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 multispec
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Części książek na temat "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
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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
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Streszczenia konferencji na temat "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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