Logo

Computer Vision: Models, Learning, and Inference

Large book cover: Computer Vision: Models, Learning, and Inference

Computer Vision: Models, Learning, and Inference
by

Publisher: Cambridge University Press
ISBN/ASIN: 1107011795
ISBN-13: 9781107011793
Number of pages: 665

Description:
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data.

Home page url

Download or read it online for free here:
Download link
(105MB, PDF)

Similar books

Book cover: Robot VisionRobot Vision
by - InTech
The purpose of robot vision is to enable robots to perceive the external world in order to perform a large range of tasks. This book presents a snapshot of the work in robot vision that is currently going on in different parts of the world.
(11968 views)
Book cover: Machine PerceptionMachine Perception
by - Prentice-Hall
This book is about visual perception. It is based on the author's experience in teaching graduate courses in the field. It assumes no previous knowledge of the field and aims to provide a comprehensive knowledge of its methods.
(15271 views)
Book cover: Machine Interpretation of Line DrawingsMachine Interpretation of Line Drawings
by - The MIT Press
The book on computer vision which solves the problem of the interpretation of line drawings and answers many other questions regarding the errors in the placement of lines in the images. Sugihara presents a mechanism that mimics human perception.
(15880 views)
Book cover: Computer VisionComputer Vision
by - Prentice Hall
The book on computer vision - the construction of explicit, meaningful descriptions of physical objects from images. Parts of the book assume some mathematical and computing background, but mainly mathematical rigor takes a backseat to concepts.
(20420 views)