High-order Models In Semantic Image Segmentation
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
|Titel:||High-order Models In Semantic Image Segmentation|
|auteur:||Ben Ayed, Ismail (professor, Departement De Genie De La Production Automatisee, Ets, Montreal, Canada)|
|Uitgever:||Elsevier Science Publishing Co Inc|
|Afmetingen:||229 x 152|
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.
Teaches readers how to apply state-of-the-art segmentation techniques