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Feature Extraction and Classification Methods of Texture Images

Ajay Kumar Singh

ISBN 10: 3659417394 / ISBN 13: 9783659417399
Editorial: LAP Lambert Academic Publishing
Nuevos Condición: New Encuadernación de tapa blanda
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Librería en AbeBooks desde: 21 de mayo de 2012

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Descripción

Paperback. 96 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. N° de ref. de la librería 9783659417399

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Detalles bibliográficos

Título: Feature Extraction and Classification ...

Editorial: LAP Lambert Academic Publishing

Encuadernación: Paperback

Condición del libro:New

Tipo de libro: Paperback

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Sinopsis:

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.

About the Author:

Asst. 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, and pursuing Phd. From MITS university, Laxmangarh, India. He has many publications books and research work.

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