UID:
almahu_9949534951102882
Format:
1 online resource (221 pages)
ISBN:
0-12-809229-7
Content:
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.
Note:
Intro -- 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.
Additional Edition:
Print version: Ben Ayed, Ismail High-Order Models in Semantic Image Segmentation San Diego : Elsevier Science & Technology,c2023 ISBN 9780128053201
Language:
English
