Towards Image Understanding : Deformable Contour and Graph-based Image Segmentation and Recognition

Lei He

ISBN 10: 3838318439 / ISBN 13: 9783838318431

Published by LAP LAMBERT Academic Publishing, 2009

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Image understanding usually includes interrelated components of image segmentation and object/scene recognition. Image segmentation extracts the objects/regions of interest from images which are then analyzed for recognition. Deformable contour methods (DCMs) are commonly applied for image segmentation. To understand the strengths and limitations of different DCMs, a comparative study to review eight major snakes and level set methods applied to the medical image segmentation is presented. The studied DCMs are compared using both qualitative and quantitative measures and the lessons learned from this medical segmentation comparison can be translated to other image segmentation domains. DCM results can be recognized for further image analysis and understanding, e.g. a graph matching algorithm is presented in this book for rather challenging segmentation applications, such as blur boundary, complex shape, and intensity inhomogeneity. The skeleton-based graph matching algorithm consists of major operations of skeleton extraction, representation, and matching for recognition, and the results are fedback into the image segmentation to increase the accuracy of the advanced segmentation.

Seller Inventory # 9783838318431

Bibliographic Details

• Title: Towards Image Understanding : Deformable Contour and Graph-based Image Segmentation and Recognition
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2009
• Language: English
• ISBN 10: 3838318439
• ISBN 13: 9783838318431
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 262 grams

About this title

Synopsis

Image understanding usually includes interrelated components of image segmentation and object/scene recognition. Image segmentation extracts the objects/regions of interest from images which are then analyzed for recognition. Deformable contour methods (DCMs) are commonly applied for image segmentation. To understand the strengths and limitations of different DCMs, a comparative study to review eight major snakes and level set methods applied to the medical image segmentation is presented. The studied DCMs are compared using both qualitative and quantitative measures and the lessons learned from this medical segmentation comparison can be translated to other image segmentation domains. DCM results can be recognized for further image analysis and understanding, e.g. a graph matching algorithm is presented in this book for rather challenging segmentation applications, such as blur boundary, complex shape, and intensity inhomogeneity. The skeleton-based graph matching algorithm consists of major operations of skeleton extraction, representation, and matching for recognition, and the results are fedback into the image segmentation to increase the accuracy of the advanced segmentation.

About the Author

Dr. Lei He received his PhD degree of Electrical Engineering at the University of Cincinnati in 2003. He is an associate professor at Armstrong Atlantic State University. His research interests include image processing and computer vision. He has published over thirty refereed journal and conference papers, secured a patent with Hewlett Packard.