- We will send in 10–14 business days.
/images/books/2816314/9780128034675.jpg)
- Author: Qiang Ji
- Publisher: Academic Press
- ISBN-10: 012803467X
- ISBN-13: 9780128034675
- Format: 19.1 x 23.5 x 1.9 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 16d.20:56:33
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Qiang Ji
- Publisher: Academic Press
- ISBN-10: 012803467X
- ISBN-13: 9780128034675
- Format: 19.1 x 23.5 x 1.9 cm, hardcover
- Language: English English
Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants.
Reviews