High-Order Models in Semantic Image Segmentation
- 1st Edition - June 29, 2023
- Latest edition
- Author: Ismail Ben Ayed
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 8 0 5 3 2 0 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 9 2 2 9 - 3
High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathemati… Read more
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
Computer scientists, electronic and biomedical engineers researching in computer vision, medical imaging, machine learning; graduate students in these fields
I. Introduction
II. Introductory Background
II.1 Discrete representations
II.1.a Graphs
II.2 Continuous representations
II.2.a Curves
II.2.b Level Sets
II.3 First-order regional terms
II.4 Second-order boundary terms
II.5 High-order terms
II.6 Convexity
II.7 Sub-modularity and super-modularity
III Basic segmentation models
III.1 Bayesian statement
III.2 The Boykov-Jolly model
III.3 The Chan-Vese model
III.4 The GrabCut model
III.5 Other parametric models
III.6 An information-theoretic view
IV Standard optimization techniques
IV.1 Gradient descent
IV.1.a Euler-Lagrange equations
IV.1.b Basic functional derivatives
IV.2 Block-coordinate descent
IV.3 Curve evolution and level sets
IV.4 Convex relaxation
IV.5 Graph Cuts
V High-order segmentation models
V.1 Entropy-based clustering
V.2 Balanced clustering
V.2.a Average association
V.2.b Normalized Cuts
V.2.c Kernel clustering
V.3 Distribution matching
V.4 Shape priors
V.4.a Generic priors
V.4.a.i Shape compactness
V.4.a.ii Shape convexity
V.4.b Specific priors
V.4.b.i Shape representations
V.4.b.ii Statistical shape models
V.5 Soft constraints
VI. Advanced optimization techniques
VI.1 Duality and linear programming relaxation
VI.2 Interior-point methods
VI.3 Trust region
VI.4 Bound and Pseudo-bound optimization
VII. Advanced medical imaging applications
VIII. Appendix
II. Introductory Background
II.1 Discrete representations
II.1.a Graphs
II.2 Continuous representations
II.2.a Curves
II.2.b Level Sets
II.3 First-order regional terms
II.4 Second-order boundary terms
II.5 High-order terms
II.6 Convexity
II.7 Sub-modularity and super-modularity
III Basic segmentation models
III.1 Bayesian statement
III.2 The Boykov-Jolly model
III.3 The Chan-Vese model
III.4 The GrabCut model
III.5 Other parametric models
III.6 An information-theoretic view
IV Standard optimization techniques
IV.1 Gradient descent
IV.1.a Euler-Lagrange equations
IV.1.b Basic functional derivatives
IV.2 Block-coordinate descent
IV.3 Curve evolution and level sets
IV.4 Convex relaxation
IV.5 Graph Cuts
V High-order segmentation models
V.1 Entropy-based clustering
V.2 Balanced clustering
V.2.a Average association
V.2.b Normalized Cuts
V.2.c Kernel clustering
V.3 Distribution matching
V.4 Shape priors
V.4.a Generic priors
V.4.a.i Shape compactness
V.4.a.ii Shape convexity
V.4.b Specific priors
V.4.b.i Shape representations
V.4.b.ii Statistical shape models
V.5 Soft constraints
VI. Advanced optimization techniques
VI.1 Duality and linear programming relaxation
VI.2 Interior-point methods
VI.3 Trust region
VI.4 Bound and Pseudo-bound optimization
VII. Advanced medical imaging applications
VIII. Appendix
- Edition: 1
- Latest edition
- Published: June 29, 2023
- Language: English
IB
Ismail Ben Ayed
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.
Affiliations and expertise
Professor, Departement de Genie de la Production Automatisee, ETS, Montreal, CanadaRead High-Order Models in Semantic Image Segmentation on ScienceDirect