feature extraction classification methods di singh ajay (8 risultati)

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In texture classification the goal is to assign an unknown sample texture image to one of a set of known texture classes.Important applications include industrial and bio medical surface inspection, for example for defects and disease, ground classification and segmentation of satellite or aerial imagery, segmentation of textured regions in document analysis, and content-based access to image databases. However, despite many potential areas of application for texture analysis in industry there is only a limited number of successful examples. A major problem is that textures in the real world are often not uniform, due to changes in orientation, scale or other visual appearance. In addition, the degree of computational complexity of many of the proposed texture measures is very high.A wide variety of techniques for describing image texture have been proposed in literature. This work is an analysis of texture image classification in different classifier under two different features called wavelet and statistical. The result shows that image classification with wavelet feature and feed forward neural network gives better result. 96 pp. Englisch.

Condizione: New. Print on Demand pp. 96 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh Ajay KumarAsst. Professor of Computer Science & Engg. Department at Mody Institute of Technology & Science Laxmangarh, India. He has graduated from CCS University Meerut and Post graduated from AAI Deemed University Allahabad, .

Condizione: New. PRINT ON DEMAND pp. 96.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In texture classification the goal is to assign an unknown sample texture image to one of a set of known texture classes.Important applications include industrial and bio medical surface inspection, for example for defects and disease, ground classification and segmentation of satellite or aerial imagery, segmentation of textured regions in document analysis, and content-based access to image databases. However, despite many potential areas of application for texture analysis in industry there is only a limited number of successful examples. A major problem is that textures in the real world are often not uniform, due to changes in orientation, scale or other visual appearance. In addition, the degree of computational complexity of many of the proposed texture measures is very high.A wide variety of techniques for describing image texture have been proposed in literature. This work is an analysis of texture image classification in different classifier under two different features called wavelet and statistical. The result shows that image classification with wavelet feature and feed forward neural network gives better result.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In texture classification the goal is to assign an unknown sample texture image to one of a set of known texture classes.Important applications include industrial and bio medical surface inspection, for example for defects and disease, ground classification and segmentation of satellite or aerial imagery, segmentation of textured regions in document analysis, and content-based access to image databases. However, despite many potential areas of application for texture analysis in industry there is only a limited number of successful examples. A major problem is that textures in the real world are often not uniform, due to changes in orientation, scale or other visual appearance. In addition, the degree of computational complexity of many of the proposed texture measures is very high.A wide variety of techniques for describing image texture have been proposed in literature. This work is an analysis of texture image classification in different classifier under two different features called wavelet and statistical. The result shows that image classification with wavelet feature and feed forward neural network gives better result.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.