High-Order Models in Semantic Image Segmentation - Product Image

High-Order Models in Semantic Image Segmentation

  • ID: 4519328
  • Book
  • 250 Pages
  • Elsevier Science and Technology
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High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging.

  • Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrations
  • Provides the right amount of knowledge to apply sophisticated techniques for a wide range of new applications
  • Contains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application
  • Presents an array of practical applications in computer vision and medical imaging
  • Includes code for many of the algorithms that is available on the book's companion website
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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
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Ben Ayed, Ismail
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.
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