Feature Extraction and Classification Methods of Texture Images: Performance Analysis of Feature Extraction Methods Under Different Classifiers - Couverture souple

Singh, Ajay Kumar; Choudhary, Dolly; Tiwari, Shamik

 
9783659417399: Feature Extraction and Classification Methods of Texture Images: Performance Analysis of Feature Extraction Methods Under Different Classifiers
Couverture souple
ISBN 10 : 3659417394 ISBN 13 : 9783659417399
Editeur : LAP LAMBERT Academic Publishing, 2013

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.

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Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2013
Langue
anglais
ISBN 10 
3659417394
ISBN 13 
9783659417399
Reliure
Broché
Nombre de pages
96
Coordonnées du fabricant
non disponible
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