High-Order Models in Semantic Image Segmentation
eBook - ePub

High-Order Models in Semantic Image Segmentation

  1. 250 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

High-Order Models in Semantic Image Segmentation

About this book

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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Yes, you can access High-Order Models in Semantic Image Segmentation by Ismail Ben Ayed in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Digital Media. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title of Book
  2. Cover image
  3. Title page
  4. Table of Contents
  5. Front Matter
  6. Copyright
  7. General introduction
  8. Chapter 1 Markov random fields
  9. Chapter 2 Graph cuts
  10. Chapter 3 Mean-field inference
  11. Chapter 4 Regularized model fitting
  12. Chapter 5 Regularized mutual information
  13. Chapter 6 Examples of high-order functionals
  14. Chapter 7 Pseudo-bound optimization
  15. Chapter 8 Trust-region optimization
  16. Chapter 9 Random field losses for deep networks
  17. Chapter 10 Constrained deep networks
  18. Index