Graph-Based Methods in Computer Vision: Developments and Applications

Xiao Bai, Jian Cheng, Edwin Hancock

Indexed In: SCOPUS View 2 More Indices

DOI: 10.4018/978-1-4666-1891-6

ISBN13: 9781466618916|ISBN10: 1466618914|EISBN13: 9781466618923

Hardcover:

Available

$195.00

Benefits & Incentives

Benefits

Printed-On-Demand (POD)
Usually ships one day from order

Hardcover:

Available

$195.00

Benefits & Incentives

Benefits

Printed-On-Demand (POD)
Usually ships one day from order

E-Book:

Available

$195.00

Benefits & Incentives

Benefits

Multi-user license (no added fee)
Immediate access after purchase
No DRM
PDF download

E-Book:

Available

$195.00

Benefits & Incentives

Benefits

Immediate access after purchase
No DRM
PDF download
Receive a 10% Discount on eBooks

Hardcover +
E-Book:

Available

$235.00

Benefits & Incentives

Benefits

Printed-On-Demand (POD)
Usually ships one day from order
Multi-user license (no added fee)
Immediate access after purchase
No DRM
PDF download

Hardcover +
E-Book:

Available

$235.00

Benefits & Incentives

Benefits

Printed-On-Demand (POD)
Usually ships one day from order
Immediate access after purchase
No DRM
PDF download

Article Processing Charge:

Available

$700.00

Benefits & Incentives

OnDemand:

(Individual Chapters)

Available

$37.50

Benefits & Incentives

Benefits

Purchase individual chapters from this book
Immediate PDF download after purchase or access through your personal library

Effective immediately, IGI Global has discontinued softcover book production. The softcover option is no longer available for direct purchase.

Description & Coverage

Description:

Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data.

Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Algorithms
Computer Vision
Data Mining
Graph-based methods
Image and video analysis
Image classification and retrieval
Image matching
Image Processing
Image Segmentation
Motion Segmentation

Reviews & Statements

Addressing problems related to applying graph-based methods in computer vision, computer, information, and other scientists present accounts of recent developments in graph-based methodology and its application to image matching, image segmentation, image and video analysis, and image processing. The topics include geometric-edge random graph model for image representation, unsupervised and supervised image segmentation using graph partitioning, generative group activity analysis with quaternion descriptor, discriminating feature selection in image classification and retrieval, and region-based graph learning towards large scale image annotation.

– Book News Inc. Portland, OR

Table of Contents

Search this Book:

Reset

Editor/Author Biographies

Dr. Bai Xiao received the B.Eng. degree in Computer Science from Beihang University (BUAA) of China in 2001. From 2002 to 2006, he was a Ph.D. student at Computer Science Department , University of York, U.K. under the supervision of Professor Edwin R. Hancock. From September 2006 to December 2008, he was a Research Officer (Fellow, Scientist) at Computer Science Department, University of Bath. He is now an Associate Professor at Computer Science School, Beihang University (BUAA). He has published more than 30 papers in journals and refreed conferences. His current research interests include computer vision, image processing and pattern recognition.

Jian Cheng is currently an associate professor of Institute of Automation, Chinese Academy of Sciences. He received the B.S. and M.S. degrees in Mathematics from Wuhan University in 1998 and in 2001, respectively. In 2004, he got his Ph.D degree in pattern recognition and intelligent systems from Institute of Automation, Chinese Academy of Sciences. From 2004 to 2006, he has been working as postdoctoral in Nokia Research Center. Then he joined National Laboratory of Pattern Recognition, Institute of Automation. His current research interests include image and video search, machine learning, etc. He has authored or co-authored more than 40 academic papers in these areas. He was awarded LU JIAXi Young Talent Prize in 2010. Dr. Cheng served as Technical Program Committee member for some international conferences, such as ACM Multimedia 2009 (content), IEEE Conference on Computer Vision and Pattern Recognition (CVPR’ 08), IEEE International Conference on Multimedia and Expo (ICME’ 08), Pacific-Rim Conference on Multimedia (PCM’ 08), IEEE International Conference on Computer Vision (ICCV’ 07), etc. He has also co-organized one special issue on Pattern Recognition Journal, and several special sessions on PCM 2008, ICME 2009, PCM 2010.

Prof. Edwin R. Hancock studied physics as an undergraduate at the University of Durham and graduated with honors in 1977. He remained at Durham to complete the PhD degree in the area of high-energy physics in 1981. Following this, he worked for 10 years as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). In 1991, he moved to the University of York as a lecturer in the Department of Computer Science. He was promoted to senior lecturer in 1997 and to reader in 1998. In 1998, he was appointed to a chair in computer vision. Professor Hancock now leads a group of some 15 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical pattern recognition. He is currently working on graph-matching, shape-from-X, image databases, and statistical learning theory. He has published more than 90 journal papers and 350 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. In 1998, he became a fellow of the International Association for Pattern Recognition. He has been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition. He has also been a guest editor for special editions of the journals Image and Vision Computing and Pattern Recognition.

Abstracting & Indexing

Archiving

All of IGI Global's content is archived via the CLOCKSS and LOCKSS initiative. Additionally, all IGI Global published content is available in IGI Global's InfoSci^® platform.