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 illustrationsProvides the right amount of knowledge to apply sophisticated techniques for a wide range of new applicationsContains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended applicationPresents an array of practical applications in computer vision and medical imagingIncludes code for many of the algorithms that is available on the book's companion website INDICE: 1. Introductory Background2. Basic segmentation models3. Standard optimization techniques4. High-order models5. Advanced optimization: Auxiliary functions and pseudo bounds6. Advanced optimization: Trust region7. Medical imaging applications8. Appendix
- ISBN: 978-0-12-805320-1
- Editorial: Academic Press
- Encuadernacion: Cartoné
- Páginas: 250
- Fecha Publicación: 01/10/2018
- Nº Volúmenes: 1
- Idioma: Inglés