High-Order Models in Semantic Image Segmentation


Language: Anglais
Cover of the book High-Order Models in Semantic Image Segmentation

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250 p. · 15.2x22.9 cm · Hardback

Finding accurately and automatically a meaningful region (or object) in an image ? whether it be an organ in a medical scan or a person in a photograph - is a subject of paramount importance in computer vision, graphics and medical imaging for its theoretical and methodological challenges, and numerous useful applications.

This book reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models underlying a broad class of recent segmentation techniques. Focusing on the algorithms which have had a significant impact in the computer vision community in the last 10 years, it focuses on the optimization of these techniques. Examples include the graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems, as well as iterative approximation techniques, by splitting extremely difficult problems into sets of easier problems.

Higher Order Functionals in Image Segmentation provides a practical and accessible introduction to these state-of ?the- art segmentation techniques for academic and industry researchers and graduate students in computer vision, machine learning and medical imaging.

  • Gives an intuitive and conceptual understanding of this mathematically involved subject, using a large number of graphical illustrations
  • Gives the right amount of knowledge to apply sophisticated techniques for a wide range of new applications, and to use and customize publicly available code for specific problems
  • Numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application
  • A rich array of practical applications in computer vision and medical imaging
  • Code for many of the algorithms available from the book?s website
1. Introductory Background
2. Basic segmentation models
3. Standard optimization techniques
4. High-order models
5. Advanced optimization: Auxiliary functions and pseudo bounds
6. Advanced optimization: Trust region
7. Medical imaging applications
8. Appendix

Computer scientists, electronic and biomedical engineers researching in computer vision, medical imaging, machine learning; graduate students in these fields.

Ismail Ben Ayed received a Ph.D. degree (with the highest honor) in the area of computer vision from the National Institute of Scientific Research (INRS-EMT), University of Quebec, Montreal, QC, Canada, in May 2007, under the guidance of Professor Amar Mitiche. Since then, he has been a research scientist with GE Healthcare, London, ON, Canada, conducting research in medical image analysis. He also holds an Adjunct Professor appointment at Western University, department of Medical Biophysics. He co-authored a book, over 50 peer-reviewed papers in reputable journals and conferences, and six patents. He received a GE recognition award in 2012 and a GE innovation award in 2010

Ismail Ben Ayed is an image segmentation and optimization expert who has authored over 60 peer-reviewed articles in the field and has co-authored the book Variational and Level Set Methods in Image Segmentation, 2011, which is receiving a high citation rate.