High-Order Models in Semantic Image Segmentation

High-Order Models in Semantic Image Segmentation

1st Edition - September 1, 2019

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  • Author: Ismail Ben Ayed
  • Hardcover ISBN: 9780128053201

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Description

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.

Key Features

  • 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

Readership

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

Table of Contents

  • 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

Product details

  • No. of pages: 250
  • Language: English
  • Copyright: © Academic Press 2029
  • Published: September 1, 2019
  • Imprint: Academic Press
  • Hardcover ISBN: 9780128053201

About the Author

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, Canada

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