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Feature Extraction and Classification Methods of Texture Images

Performance Analysis of Feature Extraction Methods Under Different Classifiers
BuchKartoniert, Paperback
96 Seiten
Englisch
LAP Lambert Academic Publishingerschienen am06.07.2013
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.mehr

Produkt

KlappentextIn 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.
Details
ISBN/GTIN978-3-659-41739-9
ProduktartBuch
EinbandartKartoniert, Paperback
Erscheinungsjahr2013
Erscheinungsdatum06.07.2013
Seiten96 Seiten
SpracheEnglisch
Gewicht144 g
Artikel-Nr.29322186

Autor

Dr. Ajay K. Singh is working as an Assistant Professor (Economics) with DIT University Dehradun. He received MPhil (Economics) from DAVV Indore and PhD (Economics) from IIT Indore. He earned Post-Doctorate in science & technology entrepreneurship from EDI of India, Ahmedabad.
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