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6 articles for “Gray Level Co-occurrence Matrix (GLCM”
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A Novel Approach for Texture Feature Extraction to Retrieve Texture Images using Gray-Level Co-occurrence Matrix (GLCM)
Abstract: Image retrieval plays an important role in many of the areas. Content based image retrieval (CBIR) is used for browsing most similar images from the large database. Texture is an important characteristics used in identifying the region of interest or objects in an image. In this image retrieval method, texture method is used to extract the features and retrieves the similar images based on similarity of the features. In the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 1, Issue 3, 2014 · pp. 32–36 Read article
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Plant Disease Detection Using SVM
Abstract: AbstractDiseases in plants are responsible for major production and economic losses and also reduction in yield. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself, it detects the symptoms of diseases. Detection on plant is very critical for defensible agriculture. It is very challenging to monitor the plant diseases physically. …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 4, Issue 3, 2017 · pp. 8–12 Read article
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Automated Thyroid Nodule Detection in Ultrasound Image using Optimal Neural Network Classifier
Abstract: Malignant thyroid nodule detection is an important issue in thyroid ultrasonography image processing. Literature reports many algorithms in this domain. This research aims to design optimal decision support system (DSS) for ultrasound thyroid nodule image characterization into benign and malignant classes. Thyroid nodule region is segmented to extract texture features from thyroid nodule ultrasound images acquired from 38 patients using gray level co-occurrence matrix (GLCM) and statistical properties. These features …
Published in Journal of Advancements in Robotics · Vol. 2, Issue 2, 2015 · pp. 15–25 Read article
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Extraction of Retinal Features in Fundus Images for Glaucoma Diagnosis
Abstract: Glaucoma is a major cause for blindness, identified early by the structural changes of optic nerve head in the retina. Retinal image analysis is emerging as an important screening tool for prior detection of eye diseases. Glaucoma is a chronic eye disease in which damage to the optic nerve leads to progressive, irreversible vision loss and it is considered as second leading cause for blindness worldwide. In this research work, …
Published in Current Trends in Information Technology · Vol. 6, Issue 1, 2016 · pp. 21–28 Read article
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A Novel Feature Level Fusion Method for Classification of Remote Sensing Images
Abstract: Feature level fusion approach is utilized in this paper to classify remote sensing images. Texture features are extracted from panchromatic images using mixed Gabor filter (GB), fast gray level co-occurrence matrix (GLCM) and linear binary pattern (LBP). The resultant texture features are classified using nearest neighbor (k-NN) classification method. Spectral features are extracted from the MS image and segmented using over segmented k-means algorithm with novel initialization (OSKNI). Finally the …
Published in Journal of Remote Sensing & GIS · Vol. 10, Issue 1, 2019 · pp. 58–65 Read article
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Leaf Disease Classification Using Artificial Neural Networks and Decision Tree Classifier
Abstract: Most of the population in India depends on agriculture production. Diseases on plant cause a significant loss of nation’s economy. The Loss can be reduced by identifying the disease at the earlier stage. Leaves are the main indicator of diseases in a plant. Hence efficient automatic leaf disease identification system is the need for the current scenario. As it detects the diseases on leaf immediately after they appear, it prevents …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 5, Issue 1, 2018 · pp. 26–32 Read article