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Relevant bibliographies by topics / GLCM ALGORITHM

Academic literature on the topic 'GLCM ALGORITHM'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "GLCM ALGORITHM"

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Saihood, Ahmed, Hossein Karshenas, and Ahmad Reza Naghsh Nilchi. "Deep fusion of gray level co-occurrence matrices for lung nodule classification." PLOS ONE 17, no. 9 (September 29, 2022): e0274516. http://dx.doi.org/10.1371/journal.pone.0274516.

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Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.

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Goel, Sajal Kumar, and Mrudula Meduri. "Ear Biometric System using GLCM Algorithm." International Journal of Information Technology and Computer Science 9, no. 10 (October 8, 2017): 68–76. http://dx.doi.org/10.5815/ijitcs.2017.10.07.

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Pietri, Olivia, Gada Rezgui, Aymeric Histace, Marine Camus, Isabelle Nion-Larmurier, Cynthia Li, Aymeric Becq, et al. "Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy." Endoscopy International Open 06, no. 04 (March 29, 2018): E462—E469. http://dx.doi.org/10.1055/a-0573-1044.

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Abstract Background and study aims Bubbles can impair visualization of the small bowel (SB) mucosa during capsule endoscopy (CE). We aimed to develop and validate a computed algorithm that would allow evaluation of the abundance of bubbles in SB-CE still frames. Patients and methods Two sets of 200 SB-CE normal still frames were created. Two experienced SB-CE readers analyzed both sets of images twice, in a random order. Each still frame was categorized as presenting with < 10 % or ≥ 10 % of bubbles. Reproducibility (κ), sensitivity (Se), specificity (Sp), receiver operating characteristic curve, and calculation time were measured for different algorithms (Grey-level of co-occurrence matrix [GLCM], fractal dimension, Hough transform, and speeded-up robust features [SURF]) using the experts’ analysis as reference. Algorithms with highest reproducibility, Se and Sp were then selected for a validation step on the second set of frames. Criteria for validation were κ = 1, Se ≥ 90 %, Sp ≥ 85 %, and a calculation time < 1 second. Results Both SURF and GLCM algorithms had high operating points (Se and Sp over 90 %) and a perfect reproducibility (κ = 1). The validation step showed the GLCM detector strategy had the best diagnostic performances, with a Se of 95.79 %, a Sp of 95.19 %, and a calculation time of 0.037 seconds per frame. Conclusion A computed algorithm based on a GLCM detector strategy had high diagnostic performance allowing assessment of the abundance of bubbles in SB-CE still frames. This algorithm could be of interest for clinical use (quality reporting) and for research purposes (objective comparison tool of different preparations).

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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 (February 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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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 (September 1, 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 value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

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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 (December 31, 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, which has been used with the multi-layer perceptron neural network classifier. The experimental results proved that, the proposed method has been succeeded to finding the best combination of GLCM parameters that leads to the best binary texture classification accuracy performance.

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Wang, Hui, Shiyu Li, Haiyang Qiu, Zhizhong Lu, Yanbo Wei, Zhiyu Zhu, and Huilin Ge. "Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images." Remote Sensing 15, no. 8 (April 14, 2023): 2078. http://dx.doi.org/10.3390/rs15082078.

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The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.

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Juneja, Jyoti, and Avani Chopra. "GLCM and PCA Algorithm based Watermarking Scheme." International Journal of Computer Applications 180, no. 48 (June 15, 2018): 24–29. http://dx.doi.org/10.5120/ijca2018917261.

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Mittal, Abhishek, Pravneet Kaur, and Dr Ashish Oberoi. "Hybrid Algorithm for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 1028–37. http://dx.doi.org/10.22214/ijraset.2022.40452.

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Abstract: The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that the introduced method performs more efficiently in comparison with the conventional techniques with regard to accuracy. Keywords: Face Spoof, KNN, Hybrid Classifier, GLCM

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Samsudin, S. A., and R. C. Hasan. "ASSESSMENT OF MULTIBEAM BACKSCATTER TEXTURE ANALYSIS FOR SEAFLOOR SEDIMENT CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W5 (October 10, 2017): 177–83. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w5-177-2017.

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Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7&thinsp;km<sup>2</sup>, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77&thinsp;% of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.

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Dissertations / Theses on the topic "GLCM ALGORITHM"

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Chang, Sheng-Mao. "A Stationary Stochastic Approximation Algorithm for Estimation in the GLMM." NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-05172007-164438/unrestricted/etd.pdf.

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Hanni, Christopher B. "Assessing palm decline in Florida by using advanced remote sensing with machine learning technologies and algorithms." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7805.

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Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing has assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of heath for trees for many years. This study provides spatial details regarding the general palm decline in Florida by taking advantage of the new developments in deep learning constructs coupled with high resolution WorldView-2 multispectral/temporal satellite imagery and LiDAR point cloud data. A novel approach using TensorFlow deep learning classification, multiband spatial statistics and indices, data reduction, and step-wise refinement masking yielded a significant improvement over Random Forest classification in a comparison analysis. The results from the TensorFlow deep learning were then used to develop an Empirical Bayesian Kriging continuous raster as an informative map regarding palm decline zones using Normalized Difference Vegetation Index Change. The significance from this research showed a large portion of the study area exhibiting palm decline and provides a new methodology for deploying TensorFlow learning for multispectral satellite imagery.

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SAPRA, PRINCE. "STUDY AND IMPLEMENTATION OF COPY-MOVE FORGERY DETECTION METHODS IN IMAGE PROCESSING." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18180.

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This research work is based on copy-move forgery detection. The copy-move forgery detection technique has various phases which include pre-processing, feature extraction and marking of forgery part on the image. In the previous methodology PCA algorithm was used for the feature reduction in the copy-move forgery detection. The parameters which are taken as input by the PCA algorithm for the feature reduction were defined statically. In this research work, to improve performance of the copy-move forgery detection model, PCA parameters needs to define dynamically. The GLCM algorithm is used for the feature extraction in this research work. The GLCM algorithm extract 13 textural features and mean of all features will be given as input to the PCA algorithm. The initial parameters of PCA algorithm will be defined dynamically for the copymove forgery detection. At last Euclidean distance is calculated between the block pixel to define forgery portion in the image. The performance of the proposed algorithm is tested in terms of precision, recall and F-measure. It is found that proposed algorithm performs better as compared to existing method in terms of all three parameters.

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Book chapters on the topic "GLCM ALGORITHM"

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Ranawat, Pratyaksha, Mayank Patel, and Ajay Kumar Sharma. "An Enhanced GLCM and PCA Algorithm for Image Watermarking." In Lecture Notes in Electrical Engineering, 885–97. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0601-7_66.

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James, Jasperine, Arunkumar Heddallikar, Pranali Choudhari, and Smita Chopde. "Analysis of Features in SAR Imagery Using GLCM Segmentation Algorithm." In Transactions on Computer Systems and Networks, 253–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1681-5_16.

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Vimal, S., Y. Harold Robinson, M. Kaliappan, K. Vijayalakshmi, and Sanghyun Seo. "Progression Detection of Glaucoma Using K-means and GLCM Algorithm." In Transactions on Computational Science and Computational Intelligence, 863–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70296-0_66.

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Vimal, S., Y. Harold Robinson, M. Kaliappan, K. Vijayalakshmi, and Sanghyun Seo. "Progression Detection of Glaucoma Using K-means and GLCM Algorithm." In Transactions on Computational Science and Computational Intelligence, 863–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70296-0_66.

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Patil, Asha, and Kalpesh Lad. "Feature Selection for Chili Leaf Disease Identification Using GLCM Algorithm." In IOT with Smart Systems, 359–65. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_35.

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Singh, Ashutosh Kumar, Rakesh Narvey, and Vishal Chaudhary. "Accurate Detection of Breast Cancer Using GLCM and LBP Features with ANN via Mammography." In Algorithms for Intelligent Systems, 593–604. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4893-6_50.

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Anand, R., T. Shanthi, R. S. Sabeenian, and S. Veni. "GLCM Feature-Based Texture Image Classification Using Machine Learning Algorithms." In Smart Computer Vision, 103–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20541-5_5.

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Josphineleela, R., and M. Ramakrishnan. "A New Classification Algorithm with GLCCM for the Altered Fingerprints." In Information Technology and Mobile Communication, 352–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20573-6_62.

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Gupta, Praveen, Nagendra Kumar, Ajad, N. Arulkumar, and Muthukumar Subramanian. "Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging." In AI and IoT-based Intelligent Health Care & Sanitation, 159–75. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815136531123010013.

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Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR – True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm.

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Swaroopa, Korla, N. Chaitanya Kumar, Christopher Francis Britto, M. Malathi, Karthika Ganesan, and Sachin Kumar. "Texture Analysis-based Features Extraction & Classification of Lung Cancer Using Machine Learning." In AI and IoT-based Intelligent Health Care & Sanitation, 114–28. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815136531123010010.

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Lung cancer is a form of carcinoma that develops as a result of aberrant cell growth or mutation in the lungs. Most of the time, this occurs due to daily exposure to hazardous chemicals. However, this is not the only cause of lung cancer; additional factors include smoking, indirect smoke exposure, family medical history, and so on. Cancer cells, unlike normal cells, proliferate inexorably and cluster together to create masses or tumors. The symptoms of this disease do not appear until cancer cells have moved to other parts of the body and are interfering with the healthy functioning of other organs. As a solution to this problem, Machine Learning (ML) algorithms are used to diagnose lung cancer. The image datasets for this study were obtained from Kaggle. The images are preprocessed using various approaches before being used to train the image model. Texture-based Feature Extraction (FE) algorithms such as Generalized Low-Rank Models (GLRM) and Gray-level co-occurrence matrix (GLCM) are then used to extract the essential characteristics from the image dataset. To develop a model, the collected features are given into ML classifiers like the Support Vector Machine (SVM) and the k-nearest neighbor's algorithm (k-NN).

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Conference papers on the topic "GLCM ALGORITHM"

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Ali, Amjad, Xiaojun Jing, and Nasir Saleem. "GLCM-based fingerprint recognition algorithm." In Multimedia Technology (IC-BNMT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icbnmt.2011.6155926.

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Ben Atitallah, M. A., R. Kachouri, M. Kammoun, and H. Mnif. "An efficient implementation of GLCM algorithm in FPGA." In 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). IEEE, 2018. http://dx.doi.org/10.1109/iintec.2018.8695275.

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Feng, G., and Ye-qing Wu. "An iris recognition algorithm based on DCT and GLCM." In Photonics Europe, edited by Peter Schelkens, Touradj Ebrahimi, Gabriel Cristóbal, and Frédéric Truchetet. SPIE, 2008. http://dx.doi.org/10.1117/12.780158.

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Ding, Xuejing. "Texture Feature Extraction Research Based on GLCM-CLBP Algorithm." In 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/emim-17.2017.36.

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Kumar, Puneet, and Suarabh Sharma. "Wet and Wrinkled Finger Recognition Using Voting Classification and GLCM Algorithm." In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021. http://dx.doi.org/10.1109/icac3n53548.2021.9725564.

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Jian Yu. "Texture segmentation based on FCM algorithm combined with GLCM and space information." In 2011 International Conference on Electric Information and Control Engineering (ICEICE). IEEE, 2011. http://dx.doi.org/10.1109/iceice.2011.5778005.

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Ramesh, S., V. Mohanavel, S. Diwakaran, Maheswaran U, and Anitha G. "Detection of critical diseases in rice crop Using GLCM Texture Feature Algorithm." In 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). IEEE, 2022. http://dx.doi.org/10.1109/accai53970.2022.9752483.

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Marleny, Finki Dona, Nurhaeni, and Bayu Nugraha. "Genetic algorithm optimization for image classification of coconut wood-based on GLCM." In THE 3RD INTERNATIONAL CONFERENCE ON ENGINEERING AND APPLIED SCIENCES (THE 3rd InCEAS) 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0107332.

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De Melo, Matheus, Andy Gajadhar, Hugo De Oliveira, Arnaldo De Andrade e Silva, and Leonardo Batista. "Analysis of Shape-Based and Texture-Based Attributes in Classification of Mammographic Findings by Machine Learning Algorithms." In ncipais do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/sbcas.2015.10364.

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Breast cancer is the most frequent cancer type among women. We present a method of classification of nodules (malignant or benign) found in mammograms using shape-based attributes and texture-based ones. Firstly, we built a test database, then we segmented and extracted a Gray Level Cooccurrence Matrix (GLCM) from each mammographic finding and analyzed texture-based and shape-based attributes. Finally, classification was performed through machine learning algorithms. Tests reached a maximum Correct Classification Rate (CCR) of 93.75%, when performed with the Radial Basis Function Network algorithm. The largest area under the ROC curve (AUC), 0.964, was achieved with the Multilayer Perceptron algorithm.

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S, Akshay, and Deepika Shetty M A. "Categorization of Fruit images using Artificial Bee Colony Algorithm based on GLCM features." In 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC). IEEE, 2022. http://dx.doi.org/10.1109/icesic53714.2022.9783611.

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