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