9783844328264 - Image Classification of Single Layered Cloud Types: A PCA based automated system to classify different types of cloud Images for better and concise forecasting of r... (9 resultados)

Condición: New. pp. 80.

Taschenbuch. Condición: Neu. Image Classification of Single Layered Cloud Types | A PCA based automated system to classify different types of cloud Images for better and concise forecasting of rain | Imran Sarwar Bajwa (u. a.) | Taschenbuch | 80 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844328264 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -An automatic classification system is presented, which discriminates the different types of single- layered clouds using Principal Component Analysis (PCA) with enhanced accuracy and provides fast processing speed as compared to other techniques. The system is first trained by cloud images. In training phase, system reads major principal features of the different cloud images to produce an image space. In testing phase, a new cloud image can be classified by comparing it with the specified image space using the PCA algorithm. Weather forecasting applications use various pattern recognition techniques to analyze clouds' information and other meteorological parameters. Neural Networks is an often-used methodology for image processing. Some statistical methodologies like FDA, RBFNN and SVM are also being used for image analysis. These methodologies require more training time and have limited accuracy of about 70%. This level of accuracy often degrades classification of clouds, and hence the accuracy of rain and other weather predictions is reduced. PCA algorithm provides a more accurate cloud classification that yield better and concise forecasting of rain. 80 pp. Englisch.

Condición: New. Print on Demand pp. 80 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 80.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bajwa Imran SarwarI am Assistant Professor in Computer Science in The Islamia University of Bahawalpur, Pakistan. I am doing my PhD in Computer Science from University of Birmingham, UK. I also worked in University of Coimbra, Po.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -An automatic classification system is presented, which discriminates the different types of single- layered clouds using Principal Component Analysis (PCA) with enhanced accuracy and provides fast processing speed as compared to other techniques. The system is first trained by cloud images. In training phase, system reads major principal features of the different cloud images to produce an image space. In testing phase, a new cloud image can be classified by comparing it with the specified image space using the PCA algorithm. Weather forecasting applications use various pattern recognition techniques to analyze clouds'' information and other meteorological parameters. Neural Networks is an often-used methodology for image processing. Some statistical methodologies like FDA, RBFNN and SVM are also being used for image analysis. These methodologies require more training time and have limited accuracy of about 70%. This level of accuracy often degrades classification of clouds, and hence the accuracy of rain and other weather predictions is reduced. PCA algorithm provides a more accurate cloud classification that yield better and concise forecasting of rain.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - An automatic classification system is presented, which discriminates the different types of single- layered clouds using Principal Component Analysis (PCA) with enhanced accuracy and provides fast processing speed as compared to other techniques. The system is first trained by cloud images. In training phase, system reads major principal features of the different cloud images to produce an image space. In testing phase, a new cloud image can be classified by comparing it with the specified image space using the PCA algorithm. Weather forecasting applications use various pattern recognition techniques to analyze clouds' information and other meteorological parameters. Neural Networks is an often-used methodology for image processing. Some statistical methodologies like FDA, RBFNN and SVM are also being used for image analysis. These methodologies require more training time and have limited accuracy of about 70%. This level of accuracy often degrades classification of clouds, and hence the accuracy of rain and other weather predictions is reduced. PCA algorithm provides a more accurate cloud classification that yield better and concise forecasting of rain.