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Artykuły w czasopismach na temat „Grey Level Co-Occurrence Matrix (GLCM)”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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7

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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Ilmawan, Fachrul, and Agung Ramadhanu. "IMPLEMENTASI METODE K-MEANS UNTUK KLASTERISASI VARIETAS PARPIKA DENGAN MENGGUNAKAN TEKNIK PENGOLAHAN CITRA DIGITAL." Jurnal Informatika Teknologi dan Sains (Jinteks) 7, no. 1 (2025): 249–54. https://doi.org/10.51401/jinteks.v7i1.5426.

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Dengan menggunakan kombinasi segmentasi objek, ekstraksi bentuk, dan ekstraksi tekstur, penelitian ini bertujuan untuk melakukan klasterisasi pada varietas paprika melalui penggunaan K-Means Clustering dan Gray-Level Co-Occurrence Matrix (GLCM). Segmentasi objek dilakukan menggunakan algoritma K-Means Clustering untuk membedakan objek dari latar belakangnya. Selanjutnya, proses ekstraksi tekstur dan bentuk dilakukan menggunakan Matriks Co-Occurrence Level Gray (GLCM) untuk membedakan jenis varietas paprika. Hasil kalsterisasi dicapai melalui penggunaan aplikasi matlab, yang mencakup import dat

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Chen, Ying, and Feng Yu Yang. "Research on Characteristic Properties of Gray Level Co-Occurrence Matrix." Applied Mechanics and Materials 204-208 (October 2012): 4755–59. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4755.

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Gray level co-occurrence matrix (GLCM) is a second-order statistical measurement. In order to understand the characterization degree of GLCM’s different feature properties, we use images of Brodatz texture images as experimental samples, analyze the change process of feature properties in horizontal, vertical and principal and secondary diagonal directions under the situation of some elements’ dynamic changes such as distance of pixels pair, size of moving window and gray level quantization,. By analyzing the experimental results, this paper can provided certain referential significance in how

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García, G., J. Maiora, M.A. Tapia, and Blas M. De. "EVALUATION OF TEXTURE FOR CLASSIFICATION OF ABDOMINAL AORTIC ANEURYSM AFTER ENDOVASCULAR REPAIR." JOURNAL OF DIGITAL IMAGING 25, no. 3 (2012): 369–76. https://doi.org/10.1007/s10278-011-9417-7.

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The use of the endovascular prostheses in abdominal aortic aneurysm has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long-term perspective, complications such as leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present, computed tomographic angiography (CTA) is the most common examination for medical surveillance. However, endoleak complications cannot always be detected by visual inspection on CTA scans. The investigation on new techniques to

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Tang, Wenhao. "Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis." EAI Endorsed Transactions on e-Learning 8, no. 2 (2023): e4. http://dx.doi.org/10.4108/eetel.v8i2.3091.

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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. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extr

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Sari, Julia Purnama, Aan Erlansari, and Endina Putri Purwandari. "Identifikasi Citra Digital Kura-Kura Sumatera Dengan Perbandingan Ekstraksi Fitur GLCM Dan GLRLM Berbasis Web." Pseudocode 8, no. 1 (2021): 66–75. http://dx.doi.org/10.33369/pseudocode.8.1.66-75.

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Kura-kura merupakan hewan yang sangat mudah dikenali karena mempunyai bentuk tubuh yang khas. Ciri khas yang dimiliki oleh kura-kura adalah adanya karapaks yang sering disebut dengan cangkang. Dalam mengidentifikasi kura-kura tidak bisa sembarangan, dibutuhkan seorang pakar yang benar-benar paham dengan spesies tersebut. Identifikasi keanekaragaman spesies kura-kura sumatera melalui pengolahan citra digital ini menggunakan metode ekstraksi fitur tekstur berbasis website. Salah satu cara mengidentifikasi jenis kura-kura yaitu dengan menggunakan sistem identifikasi secara otomatis berbasis pemro

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Gupta, Chaahat, Naveen Kumar Gondhi, and Parveen Kumar Lehana. "Gray Level Co-Occurrence Matrix (GLCM) Parameters Analysis for Pyoderma Image Variants." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 353–58. http://dx.doi.org/10.1166/jctn.2020.8674.

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Analysis of different visual textures present in the given images is one of the important perspectives of human vision for objects segregation and identification. Texture-based features are widely used in medical diagnosis for informal prediction of dermatological diseases. Dermatological diseases are the most universal diseases affecting all the living beings worldwide. Recent advancements in image processing have considerably improved the classification, identification, and treatment of various dermatological diseases. Present paper reports the results of Gray Level Co-occurrence Matrix (GLC

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Suharjito, Imran Bahtiar, and Girsang Suganda. "Family Relationship Identification by Using Extract Feature of Gray Level Co-occurrence Matrix (GLCM) Based on Parents and Children Fingerprint." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2738–45. https://doi.org/10.11591/ijece.v7i5.pp2738-2745.

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This study aims to find out the relations correspondence by using Gray Level Co-occurrence Matrix (GLCM) feature on parents and children finger print. The analysis is conducted by using the finger print of parents and family in one family There are 30 families used as sample with 3 finger print consists of mothers, fathers, and children finger print. Fingerprints data were taken by fingerprint digital persona u are u 4500 SDK. Data analysis is conducted by finding the correlation value between parents and children fingerprint by using correlation coefficient that gained from extract feature GL

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Eichkitz, Christoph Georg, Marcellus Gregor Schreilechner, Paul de Groot, and Johannes Amtmann. "Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes." Interpretation 3, no. 1 (2015): T13—T23. http://dx.doi.org/10.1190/int-2014-0099.1.

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Texture attributes describe the spatial arrangement of neighboring amplitudes values within a given analysis window. We chose a statistical texture classification method, the gray-level co-occurrence matrix (GLCM), and its derived attributes, to produce a semiautomated description of the spatial arrangement of seismic facies. The GLCM is a measure of how often different combinations of neighboring pixel values occur. We tested the application of directional GLCM-based attributes for the detection of seismic variability within paleoriver features. Calculation of 3D GLCM-based attributes can be

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Widyaningsih, Maura. "Identifikasi Kematangan Buah Apel Dengan Gray Level Co-Occurrence Matrix (GLCM)." Jurnal SAINTEKOM 6, no. 1 (2017): 71. http://dx.doi.org/10.33020/saintekom.v6i1.7.

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Digital image processing is part of the technological developments in the concepts and reasoning, the human wants the machine (computer) can recognize images like human vision. Recognizing the image is one way to distinguish the traits that exist in the image. Texture is one of the characteristics that distinguish the image, is the basic characteristic of the image identification. Gray Level Co-Occurrence Matrix (GLCM) is one method of obtaining characteristic texture image by calculating the probability of adjacency relationship between two pixels at a certain distance and direction. The char

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Karnati, Srivathsav, and Biswajit Pathak. "Analysis of biospeckle pattern using grey-level and color-channel assessment methods." Laser Physics 34, no. 10 (2024): 105601. http://dx.doi.org/10.1088/1555-6611/ad7720.

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Abstract Biospeckle offers a practical tool for contact-free testing and monitoring of biological samples, providing unique insights into dynamics of biological processes. In the present work, we design an experimental arrangement to perform quality assessment on biological samples using biospeckle patterns. We analyse the speckle patterns and evaluate its important parameters by constructing a grey-level co-occurrence matrix (GLCM). Furthermore, we propose an alternative and reliable method to study the biospeckle patterns by constructing a color-channel assessment matrix. The proposed approa

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Pantic, Igor, Sanja Dacic, Predrag Brkic, et al. "Application of Fractal and Grey Level Co-Occurrence Matrix Analysis in Evaluation of Brain Corpus Callosum and Cingulum Architecture." Microscopy and Microanalysis 20, no. 5 (2014): 1373–81. http://dx.doi.org/10.1017/s1431927614012811.

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AbstractThis aim of this study was to assess the discriminatory value of fractal and grey level co-occurrence matrix (GLCM) analysis methods in standard microscopy analysis of two histologically similar brain white mass regions that have different nerve fiber orientation. A total of 160 digital micrographs of thionine-stained rat brain white mass were acquired using a Pro-MicroScan DEM-200 instrument. Eighty micrographs from the anterior corpus callosum and eighty from the anterior cingulum areas of the brain were analyzed. The micrographs were evaluated using the National Institutes of Health

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Di, Haibin, and Dengliang Gao. "Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation." Interpretation 5, no. 3 (2017): SJ31—SJ40. http://dx.doi.org/10.1190/int-2016-0214.1.

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Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the o

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Eichkitz, Christoph Georg, Johannes Amtmann, and Marcellus Gregor Schreilechner. "Computation of grey level co-occurrence matrix (GLCM) attributes in single and multiple directions." First Break 38, no. 3 (2020): 57–62. http://dx.doi.org/10.3997/1365-2397.fb2020019.

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Ou, Xiang, Wei Pan, and Perry Xiao. "In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM)." International Journal of Pharmaceutics 460, no. 1-2 (2014): 28–32. http://dx.doi.org/10.1016/j.ijpharm.2013.10.024.

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Mahrus ali, Muhammad Mahrus. "DETEKSI JALAN BERLUBANG MENGGUNAKAN METODE GREY LEVEL CO-OCCURRENCE MATRIX DAN NEURAL NETWORK." COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi 3, no. 1 (2022): 01–08. http://dx.doi.org/10.33650/coreai.v3i1.4088.

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Faktor utama yang menjadi penentu kelayakan kondisi suatu jalan yaitu kerusakan pada aspal sehingga pemeliharaan jalan perlu dilakukan secara berkala. Pemeriksaan kondisi jalan dilakukan oleh petugas survey dengan melakukan pengamatan langsung pada jalan yang akan diberikan penilaian secara manual. Aktifitas pemeriksaan dapat mengganggu kelancaran arus lalu lintas pada jalan yang padat kendaraan terlebih lagi dapat membahayakan keselamatan petugas survey. Diperlukan alternative pemeriksaan jalan untuk menghindari ancaman yang tidak diinginkan dan dapat membiat biaya lebih efektif. Pada penelit

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Chen, Ying, and Feng Yu Yang. "Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix." Applied Mechanics and Materials 204-208 (October 2012): 4746–50. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4746.

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Gray level co-occurrence matrix (GLCM) is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction, local neighborhood and magnitude of changes. Firstly, we analyze and reveal the generation process of gray level co-occurrence matrix from horizontal, vertical and principal and secondary diagonal directions. Secondly, we use Brodatz texture images as samples, and analyze the relationship between non-zero elements of gray level co-occurrence matrix in changes of both direction and distances of each pixels pair by. Fina

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Sri Aprillia, Bandiyah, Achmad Rizal, and Muhammad Arik Geraldy Fauzi. "Grey Level Differences Matrix for Alcoholic EEG Signal Classification." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024): 26. http://dx.doi.org/10.62527/joiv.8.1.2602.

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Electroencephalogram (EEG) signals can provide information on abnormalities in a person's brain and characterize brain activity. Brain injury or diseases can manifest as brain disorders. Trauma or the use of specific chemicals or medications, such as alcohol, can result in brain damage. Previous research has demonstrated variations in the patterns of EEG signals between alcohol-using and non-drinking people. Various techniques, including wavelet and entropy, have been developed to detect alcoholic EEG using event-related potential (ERP) testing. This work proposes a feature extraction techniqu

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Jain, Palak. "FPGA-Based Satellite Vision Systems using Verilog HDL." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 879–84. http://dx.doi.org/10.22214/ijraset.2024.58850.

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Abstract: It is believed that the most effective way to collect information about the Earth's surface is through high- quality satellite images. Extracting a feature from an image is really difficult because you have to choose the best image segmentation methods and combine many strategies to find the Region in the most effective manner. This study makes recommendations for the classification techniques for objects in the satellite. On high-resolution satellite images, applying image processing methods. The methods used to define region mostly focus on urban, agricultural, and forest regions.

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Ramadhani, Faundra Zahwa, Hari Purwadi, and Ansar Rizal. "Clustering K-Means Berdasarkan Ciri Gray Level Co-occurrence Matrix Pada Foto Wajah." Jurnal Komputer, Informasi dan Teknologi 5, no. 1 (2025): 10. https://doi.org/10.53697/jkomitek.v5i1.2498.

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Penelitian ini bertujuan untuk mengetahui Ciri Citra Wajah Berdasarkan Gray Level Coocurence Matrik (Glcm). Dan melakukan pengelompokan klaster menggunakan metode Simpel K- Means untuk mengetahui clustered instances. Tahapan dimulai dari pengambilan gambar wajah menggunakan kamera smartphone dengan jarak 100cm dan pencahayaan yang dikendalikan. Gambar kemudian diubah dari format RGB ke grayscale sebagai langkah awal pengolahan. Matriks GLCM dibentuk berdasarkan empat orientasi sudut (0°, 45°, 90°, dan 135°), dan dari matriks tersebut diekstraksi empat ciri tekstur utama, yaitu contrast, correl

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Abdulla, Beshaier A., Yossra H. Ali, and Nuha J. Ibrahim. "Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments." Engineering and Technology Journal 38, no. 5A (2020): 719–27. http://dx.doi.org/10.30684/etj.v38i5a.519.

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In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are us

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Bhakti, Adhitiyah Redaya Kusuma, Abduh Riski, and Ahmad Kamsyakawuni. "Sistem Biometrik Pengenalan Wajah dengan Metode Grey Level Co-Occurrence Matrix dan Support Vector Machine." Indonesian Journal of Applied Informatics 7, no. 2 (2024): 112. http://dx.doi.org/10.20961/ijai.v7i2.69069.

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<p><strong><span lang="EN-US">Abstrak </span></strong></p><p><span lang="EN-US">Teknologi biometrik wajah dikembangkan untuk mengenali seseorang secara unik. Pada penelitian ini biometrik diaplikasikan pada aplikasi pengenalan wajah dengan citra wajah manusia sebagai objeknya menggunakan metode Grey Level Co-Occurrence Matrix dan Support Vector Machine. Metode GLCM merupakan metode yang digunakan untuk proses ekstraksi fitur citra. Sedangkan SVM digunakan untuk proses pengenalan/identifikasi. Tujuan dari penelitian ini adalah mendapat hasil akura

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Nugroho, Herminarto, Wahyu Agung Pramudito, and Handoyo Suryo Laksono. "Gray Level Co-Occurrence Matrix (GLCM)-based Feature Extraction for Rice Leaf Diseases Classification." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 4 (2025): 392–400. https://doi.org/10.12928/biste.v6i4.9286.

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In this paper, we propose Gray Level Co-Occurrence Matrix (GLCM) based Feature Extraction to identify and classify rice leaf diseases. An Artificial Neural Network (ANN) algorithm is used to train a classification model. Various statistical features such as energy, contrast, homogeneity, and correlation are extracted from the GLCM matrix to describe the image texture features. After feature removal, an ANN classification model was trained using a dataset consisting of images of healthy and diseased rice leaves. The ANN training process involves optimizing weights and bias using backpropagation

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A. Rahim, Abd Mizwar, and Theopilus Bayu Sasongko. "Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 24, no. 2 (2025): 219–34. https://doi.org/10.30812/matrik.v24i2.4035.

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This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the d

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Blanco, A. C., J. B. Babaan, J. E. Escoto, and C. K. Alcantara. "MODELLING OF LAND SURFACE TEMPERATURE USING GRAY LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST REGRESSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 23–28. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-23-2020.

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Abstract. Modelling of land surface temperature (LST) is conducted to be able to explain the spatial and temporal variations of LST using a set of explanatory variables. LST in a previous study was modelled as a linear function of vegetation cover and built up cover as quantified by the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI), respectively, and other variables, namely, albedo, solar radiation (SR), surface area-volume ratio (SVR), and skyview factor (SVF). SVF requires a digital surface model of sufficient resolution while SVR computati

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PANTIC, IGOR, SENKA PANTIC, JOVANA PAUNOVIC, and MILAN PEROVIC. "Nuclear entropy, angular second moment, variance and texture correlation of thymus cortical and medullar lymphocytes: Grey level co-occurrence matrix analysis." Anais da Academia Brasileira de Ciências 85, no. 3 (2013): 1063–72. http://dx.doi.org/10.1590/s0001-37652013005000045.

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Grey level co-occurrence matrix analysis (GLCM) is a well-known mathematical method for quantification of cell and tissue textural properties, such as homogeneity, complexity and level of disorder. Recently, it was demonstrated that this method is capable of evaluating fine structural changes in nuclear structure that otherwise are undetectable during standard microscopy analysis. In this article, we present the results indicating that entropy, angular second moment, variance, and texture correlation of lymphocyte nuclear structure determined by GLCM method are different in thymus cortex when

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Mrs.S.Gandhimathi, @. Usha, Rani M.Jeya, S.Sneha, and R.Kalaiselvi. "FEATURE EXTRACTION BASED RETRIEVAL OF GEOGRAPHIC IMAGES." International Journal of Computational Science and Information Technology (IJCSITY) 2, May (2019): 1–9. https://doi.org/10.5281/zenodo.3532468.

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<strong>ABSTRACT </strong> This project is to retrieve the similar geographic images from the dataset based on the features extracted. Retrieval is the process of collecting the relevant images from the dataset which contains more number of images. Initially the preprocessing step is performed in order to remove noise occurred in input image with the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the features from the images. After this process, the

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Albkosh, Fthi M. A., Alsadegh S. S. Mohamed, Ali A. Elrowayati, and Mamamer M. Awinat. "Features Optimization of Gray Level Co-Occurrence Matrix by Artificial Bee Colony Algorithm for Texture Classification." مجلة الجامعة الأسمرية: العلوم التطبيقية 6, no. 5 (2021): 839–57. http://dx.doi.org/10.59743/aujas.v6i5.1294.

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Gray Level Co-occurrence Matrix (GLCM) is one of the most popular texture analysis methods. The fundamental issue of GLCM is the suitable selection of input parameters, where many researchers depended on trial and observation approach for selecting the best combination of GLCM parameters to improve the texture classification, which is tedious and time-consuming. This paper proposes a new optimization method for the GLCM parameters using Artificial Bee Colony Algorithm (ABC) to improve the binary texture classification. For the testing, 13 Haralick features were extracted from the UMD database,

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Shelke, Mrs Vishakha, Mr Vinay Manish Shah, Mr Harsh Ratnani, and Mr Rahul Despande. "Diabetic Retinopathy Detection Using SVM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 868–75. http://dx.doi.org/10.22214/ijraset.2022.41275.

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Abstract: Innovation is getting progressed step by step in pretty much every field. This work includes the detection of Diabetic Retinopathy (DR). Diabetes happens when the pancreas neglects to emit sufficient insulin, and gradually influences the retina of the natural eye. As it advances, the vision of a patient begins deteriorating (depleting), prompting diabetic retinopathy. In such manner, retinal pictures gained through fundal camera help in investigating the outcomes, nature, and status of the impact of diabetes on the eye. The main aim of this study is Age-related Macular Degeneration (

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MUSIAFA, ZAYID. "PERANCANGAN EKSTRAKSI FITUR MOTIF SASIRANGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES BERBASIS COLOR HISTOGRAM DAN GRAY LEVEL CO-OCCURRENCE MATRICES (GLCM)." Technologia: Jurnal Ilmiah 8, no. 2 (2017): 108. http://dx.doi.org/10.31602/tji.v8i2.1114.

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Sasirangan adalah kain adat suku Banjar di Kalimantan Selatan yang dibuat dengan teknik tusuk jelujur. Penelitian menggunakan uji algoritma Naive Bayes Klasifikasi terhadap citra kain sasirangan yang diekstrak dengan metode berbasis color histogram dan GLCM data terdiri dari 30 citra digital kain sasirangan terdiri dari 10 data citra motif Hiris Gagatas dengan label g class 0, 10 data citra motif Kulat Kurikit diberi label k class 1, dan 10 data citra motif Absrak diberi label a class 2. Pengujian data menggunakan X-Validation dengan ketentuan Number Validaton uji 10 sampai dengan 2, type vali

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ARORA, VINAY, EDDIE YIN-KWEE NG, ROHAN SINGH LEEKHA, KARUN VERMA, TAKSHI GUPTA, and KATHIRAVAN SRINIVASAN. "HEALTH OF THINGS MODEL FOR CLASSIFYING HUMAN HEART SOUND SIGNALS USING CO-OCCURRENCE MATRIX AND SPECTROGRAM." Journal of Mechanics in Medicine and Biology 20, no. 06 (2020): 2050040. http://dx.doi.org/10.1142/s0219519420500402.

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Cardiovascular diseases have become one of the world’s leading causes of death today. Several decision-making systems have been developed with computer-aided support to help the cardiologists in detecting heart disease and thereby minimizing the mortality rate. This paper uses an unexplored sub-domain related to textural features for classifying phonocardiogram (PCG) as normal or abnormal using Grey Level Co-occurrence Matrix (GLCM). The matrix has been applied to extract features from spectrogram of the PCG signals taken from the Physionet 2016 benchmark dataset. Random Forest, Support Vector

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Yan, L., and W. Xia. "A MODIFIED THREE-DIMENSIONAL GRAY-LEVEL CO-OCCURRENCE MATRIX FOR IMAGE CLASSIFICATION WITH DIGITAL SURFACE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 133–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-133-2019.

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<p><strong>Abstract.</strong> 2D texture cannot reflect the 3D object’s texture because it only considers the intensity distribution in the 2D image region but int real world the intensities of objects are distributed in 3D surface. This paper proposes a modified three-dimensional gray-level co-occurrence matrix (3D-GLCM) which is first introduced to process volumetric data but cannot be used directly to spectral images with digital surface model because of the data sparsity of the direction perpendicular to the image plane. Spectral and geometric features combined with no te

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Li, Min, and Jian Jun Liao. "Texture Image Segmentation Based on GLCM." Applied Mechanics and Materials 220-223 (November 2012): 1398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1398.

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The paper proposed a method on marble texture image segmentation based on Gray Level Co-occurrence Matrix (GLCM). At first, compute the Contrast matrix on basis of GLCM. Then choose the maximum of the matrix as the threshold to segment the object. At last extract the object contour with curve fitting method. Experiment results show that the method is accuracy.

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Shi, Huilan, Junya Jia, Dong Li, Li Wei, Wenya Shang, and Zhenfeng Zheng. "Blood oxygen level-dependent magnetic resonance imaging for detecting pathological patterns in patients with lupus nephritis: a preliminary study using gray-level co-occurrence matrix analysis." Journal of International Medical Research 46, no. 1 (2017): 204–18. http://dx.doi.org/10.1177/0300060517721794.

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Objective Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) is a noninvasive technique useful in patients with renal disease. The current study was performed to determine whether BOLD MRI can contribute to the diagnosis of renal pathological patterns. Methods BOLD MRI was used to obtain functional magnetic resonance parameter R2* values. Gray-level co-occurrence matrixes (GLCMs) were generated for gray-scale maps. Several GLCM parameters were calculated and used to construct algorithmic models for renal pathological patterns. Results Histopathology and BOLD MRI were used to ex

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Prakash, Shubhra, and B. Ramamurthy. "Gray Level Co-occurrence Matrix based Fully Convolutional Neural Network Model for Pneumonia Detection." International journal of electrical and computer engineering systems 15, no. 4 (2024): 369–76. http://dx.doi.org/10.32985/ijeces.15.4.7.

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This study presents a new method to improve the detection ability of a convolutional neural network (CNN) in pneumonia detection using chest X-ray images. Using Gray-Level Co-occurrence Matrix (GLCM) analysis, additional channels are added to the original image data provided by Guangzhou Children's Hospital in Guangzhou, China. The main goal is to design a lightweight, fully convolution network and increase its available information using GLCM. Performance analysis is performed on the new CNN model and GLCM-enhanced CNN model, and results are compared with Transfer Learning approaches. Various

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Isnanto, R. Rizal, Munawar Agus Riyadi, and Muhammad Fahmi Awaj. "Herb Leaves Recognition using Gray Level Co-occurrence Matrix and Five Distance-based Similarity Measures." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1920. http://dx.doi.org/10.11591/ijece.v8i3.pp1920-1932.

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Herb medicinal products derived from plants have long been considered as an alternative option for treating various diseases. In this paper, the feature extraction method used is Gray Level Co-occurrence Matrix (GLCM), while for its recognition using the metric calculations of Chebyshev, Cityblock, Minkowski, Canberra, and Euclidean distances. The method of determining the GLCM Analysis based on the texture analysis resulting from the extraction of this feature is Angular Second Moment, Contrast, Inverse Different Moment, Entropy as well as its Correlation. The recognition system used 10 leaf

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Anggraini, Chintya, and Sriani. "KLASIFIKASI DAUN KELENGKENG MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN K-NEAREST NEIGHBOR (KNN)." JSiI (Jurnal Sistem Informasi) 11, no. 2 (2024): 72–78. http://dx.doi.org/10.30656/jsii.v11i2.9157.

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Tanaman kelengkeng (Dimocarpus longan) termasuk dalam jenis tanaman buah dengan nilai ekonomi tinggi dan menjadi komoditas penting dalam sektor pertanian. Kelengkeng memiliki berbagai varietas yang beragam berdasarkan ciri-ciri khas dari masing-masing jenis yang cukup sulit dibedakan, terutama bagi orang awam. Berdasarkan permasalahan dalam menentukan jenis tanaman kelengkeng, maka perlu adanya sistem yang dapat mengklasifikasikan jenis tanaman kelengkeng. Penelitian ini mengusulkan ekstraksi fitur tekstur dari citra daun kelengkeng dengan memanfaatkan Gray Level Co-occurrence Matrix (GLCM), d

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Salsabiilaa, Rizka Kaamtsaalil. "DETEKSI KUALITAS DAN KESEGARAN TELUR AYAM RAS BERDASARKAN DETEKSI OBJEK TRANSPARAN DENGAN METODE GREY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN KLASIFIKASI K-NEAREST NEIGHBOR (KNN)." TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika 1, no. 2 (2019): 1. http://dx.doi.org/10.25124/tektrika.v1i2.1740.

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Telur adalah salah satu bahan pangan yang mudah dan lazim dijumpai di masyarakat Indonesia. Selain harganya murah, telur merupakan sumber nutrisi penting bagi kesehatan tubuh. Namun telur memiliki kualitas dan kesegaran yang berbeda-beda tergantung pada lingkungan penyimpanan dan kondisi induknya. Kesegaran telur dapat diketahui dari ketebalan dan kekentalan putih telurnya. Semakin tinggi putih telur semakin segar telur tersebut. Tebal atau tinggi albumen dapat diketahui dari nilai HU (Haugh Unit). Dalam makalah ini penulis membahas mengenai cara mendeteksi kualitas dan kesegaran telur menggun

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Chen, Zi Xin, and Feng Yu Xu. "Application of Gray Level Co-Occurrence Matrix Method in Characterization of Cylindrical Grinding Surface Roughness." Applied Mechanics and Materials 433-435 (October 2013): 2113–16. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2113.

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Machine vision based surface roughness inspection method is applied to assess different cylindrical grinding surfaces under LED illumination. Images directly recorded by a camera are analyzed by gray level co-occurrence matrix (GLCM) method to discover its texture information. It shows obviously relationship between feature values of the matrix and their corresponding surface roughness values. Uniform table are also designed to choose optimal parameters, which is five of distance between pixel pairs and ninety degree of angle to calculate GLCM. Entropy is chosen to represent different surface

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Zhu, Dandan, Ruru Pan, Weidong Gao, and Jie Zhang. "Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM." Autex Research Journal 15, no. 3 (2015): 226–32. http://dx.doi.org/10.1515/aut-2015-0001.

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Abstract In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold valu

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Arifin, Arifin, and Abdul Rahman. "Identifikasi Kualitas Kerabang Telur Ayam Dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix." MDP Student Conference 2, no. 1 (2023): 250–56. http://dx.doi.org/10.35957/mdp-sc.v2i1.4276.

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Telur merupakan salah satu hasil ternak yang mengandung komponen gizi yang lengkap seperti protein, lemak, vitamin dan mineral. Selain itu, telur juga banyak diminati masyarakat dikarenakan harga nya yang terjangkau dan mempunyai ketersediaan yang cukup banyak. Namun tidak semua telur mempunyai kualitas yang baik, sehingga pada penelitian ini dilakukan identifikasi kualitas kerabang telur dimana kualitas kerabang telur dibagi menjadi tiga golongan yakni Gol 1,Gol 2, dan Gol 3 untuk tiap jarak potret 8 cm, 12 cm dan 16 cm yang kemudian menerapkan Gray Level Co-Occurrence Matrix (GLCM) sebagai e

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