Feature Extraction and Classification Methods of Texture Images | Performance Analysis of Feature Extraction Methods Under Different Classifiers

Ajay Kumar Singh (u. a.)

ISBN 10: 3659417394 / ISBN 13: 9783659417399

Published by LAP LAMBERT Academic Publishing, 2013

About this Item

Feature Extraction and Classification Methods of Texture Images | Performance Analysis of Feature Extraction Methods Under Different Classifiers | Ajay Kumar Singh (u. a.) | Taschenbuch | 96 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659417399 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 105748614

Bibliographic Details

• Title: Feature Extraction and Classification Methods of Texture Images | Performance Analysis of Feature Extraction Methods Under Different Classifiers
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2013
• Language: English
• ISBN 10: 3659417394
• ISBN 13: 9783659417399
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 161 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.