Knowledge modeling of image analysis and interpretation | A Unified Framework for Weldment Defect Detection using X-Ray images through Image Processing and Knowledge Modeling

Margret S. Anouncia (u. a.)

ISBN 10: 3659245445 / ISBN 13: 9783659245442

Published by LAP Lambert Academic Publishing, 2012

About this Item

Knowledge modeling of image analysis and interpretation | A Unified Framework for Weldment Defect Detection using X-Ray images through Image Processing and Knowledge Modeling | Margret S. Anouncia (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783659245442 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106262411

Bibliographic Details

• Title: Knowledge modeling of image analysis and interpretation | A Unified Framework for Weldment Defect Detection using X-Ray images through Image Processing and Knowledge Modeling
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659245445
• ISBN 13: 9783659245442
• Binding: Taschenbuch
• Condition: Neu
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book focuses on design and development of a unified framework for radiographic image analysis and interpretation with emphasis on defect classification. The framework is constructed by creating two ontologies namely process ontology and domain ontology. Process ontology structures information for performing gray scale image analysis and it is maintained as a knowledgebase. The images are analyzed by using the process plan which resulted in the enhanced images from which geometrical, statistical and textural features are extracted to construct domain ontology. In addition to that, details of welding defects in radiographic image are conceptualized and maintained in the domain ontology. The framework includes a flexible user interface through which the experts’ knowledge of the domain can be uploaded into the knowledgebase. The knowledgebase search is minimized into single selection on the domain ontology using which the entire content of defects and type of defect appearing in the image can be visualized. The performance study shows that the developed unified framework outperformed the other two models viz. neural and statistical with respect to classification of defects.

About the Author

Dr. S. Margret Anouncia, B.E. (CSE), M.E. (S/W Engg.), Ph.D (CSE) Professor, School of Computing Sciences, VIT University, India, works on Digital Image Processing, Software Engineering and Knowledge Engineering. Have around 18 years of teaching / research experience. Handled funded research projects on A.I.,Image processing and knowledge modeling.