High-Order Models in Semantic Image Segmentation

Langue : Anglais

Auteur : Ben Ayed Ismail

Couverture de l’ouvrage High-Order Models in Semantic Image Segmentation

Résumé
Sommaire
Lectorat
Biographie
Commentaire

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.

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.

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