The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Prabir Bhattacharya - One of the best experts on this subject based on the ideXlab platform.
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particulate matter characterization by gray level co Occurrence Matrix based support vector machines
Journal of Hazardous Materials, 2012Co-Authors: K Manivannan, Priyanka Aggarwal, Vijay Devabhaktuni, Ashok Kumar, Douglas Nims, Prabir BhattacharyaAbstract:An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-Occurrence Matrix (GLCM) of the image. This Matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
K Manivannan - One of the best experts on this subject based on the ideXlab platform.
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particulate matter characterization by gray level co Occurrence Matrix based support vector machines
Journal of Hazardous Materials, 2012Co-Authors: K Manivannan, Priyanka Aggarwal, Vijay Devabhaktuni, Ashok Kumar, Douglas Nims, Prabir BhattacharyaAbstract:An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-Occurrence Matrix (GLCM) of the image. This Matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
Vijay Devabhaktuni - One of the best experts on this subject based on the ideXlab platform.
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particulate matter characterization by gray level co Occurrence Matrix based support vector machines
Journal of Hazardous Materials, 2012Co-Authors: K Manivannan, Priyanka Aggarwal, Vijay Devabhaktuni, Ashok Kumar, Douglas Nims, Prabir BhattacharyaAbstract:An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-Occurrence Matrix (GLCM) of the image. This Matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
Priyanka Aggarwal - One of the best experts on this subject based on the ideXlab platform.
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particulate matter characterization by gray level co Occurrence Matrix based support vector machines
Journal of Hazardous Materials, 2012Co-Authors: K Manivannan, Priyanka Aggarwal, Vijay Devabhaktuni, Ashok Kumar, Douglas Nims, Prabir BhattacharyaAbstract:An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-Occurrence Matrix (GLCM) of the image. This Matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
Ashok Kumar - One of the best experts on this subject based on the ideXlab platform.
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particulate matter characterization by gray level co Occurrence Matrix based support vector machines
Journal of Hazardous Materials, 2012Co-Authors: K Manivannan, Priyanka Aggarwal, Vijay Devabhaktuni, Ashok Kumar, Douglas Nims, Prabir BhattacharyaAbstract:An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-Occurrence Matrix (GLCM) of the image. This Matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
