Computer Vision: Models, Learning, and Inference
by Simon J.D. Prince
Publisher: Cambridge University Press 2012
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.
Download or read it online for free here:
Download link
(105MB, PDF)
Similar books

by Julio Ponce, Adem Karahoca - IN-TECH
Nearest feature classification for face recognition, subspace methods, a multi-stage classifier for face recognition undertaken by coarse-to-fine strategy, PCA-ANN face recognition system based on photometric normalization techniques, etc.
(9937 views)

by Joachim Weickert - Teubner
Many recent techniques for digital image enhancement and multiscale image representations are based on nonlinear PDEs. This book gives an introduction to the main ideas behind these methods, and it describes in a systematic way their foundations.
(12455 views)

by Jan Erik Solem - O'Reilly Media
The idea behind this book is to give an easily accessible entry point to hands-on computer vision with enough understanding of the underlying theory and algorithms to be a foundation for students, researchers and enthusiasts.
(22568 views)

by Dilip K. Prasad - arXiv
We propose a new object detection/recognition method, which improves over the existing methods in every stage of the object detection/recognition process. In addition to the usual features, we propose to use geometric shapes as additional features.
(7407 views)