Limited Offer
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
High-Order Models in Semantic Image Segmentation
- 1st Edition - June 16, 2023
- 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
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteHigh-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
- Cover image
- Title page
- Table of Contents
- Copyright
- General introduction
- General context
- From graphical models to deep learning
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- Chapter 10
- Chapter 1: Markov random fields
- Abstract
- 1.1. Discrete representations
- 1.2. Popular optimizers for random fields
- References
- Chapter 2: Graph cuts
- Abstract
- 2.1. Min-cut and max-flow problems
- 2.2. Move-making algorithms for multi-label problems
- References
- Chapter 3: Mean-field inference
- Abstract
- 3.1. Pairwise conditional random field functions
- 3.2. Mean-field inference
- Appendix 3.A.
- References
- Chapter 4: Regularized model fitting
- Abstract
- 4.1. General probabilistic form
- 4.2. Standard models
- References
- Chapter 5: Regularized mutual information
- Abstract
- 5.1. Model fitting as entropy minimization
- 5.2. Limitations of entropy and highly descriptive models
- 5.3. A discriminative view of the mutual information
- References
- Chapter 6: Examples of high-order functionals
- Abstract
- 6.1. Introduction
- 6.2. Shape priors
- 6.3. Graph clustering
- 6.4. Distribution matching
- References
- Chapter 7: Pseudo-bound optimization
- Abstract
- 7.1. Bound optimization
- 7.2. Bound optimization
- 7.3. Pseudo-bound optimization
- 7.4. Auxiliary functionals
- References
- Chapter 8: Trust-region optimization
- Abstract
- 8.1. General-form problem
- 8.2. Trust-region optimization
- 8.3. A shape prior example
- 8.4. Details of the Gateâux derivatives
- References
- Chapter 9: Random field losses for deep networks
- Abstract
- 9.1. Fully supervised segmentation
- 9.2. Weakly supervised segmentation
- 9.3. Beyond gradient descent for random field losses
- References
- Chapter 10: Constrained deep networks
- Abstract
- 10.1. Weakly supervised segmentation via constrained CNNs
- 10.2. Constraint optimization
- 10.3. Discussion of some experimental results
- References
- Index
- No. of pages: 250
- Language: English
- Edition: 1
- Published: June 16, 2023
- Imprint: Academic Press
- Hardback ISBN: 9780128053201
- eBook ISBN: 9780128092293
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