Computer Vision: Models, Learning, and Inference
by Simon J.D. Prince
Publisher: Cambridge University Press 2012
Number of pages: 665
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:
by Jose R.A. Torreao - InTech
In this small book the authors have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints.
by David Vernon - Prentice Hall
This book is a comprehensive introduction to machine vision, it will allow the reader to quickly comprehend the essentials of this topic. Emphasis is on a range of the tools and techniques for image acquisition, processing, and analysis.
by Pei-Gee Ho - InTech
The objective of the image segmentation is to simplify the representation of pictures into meaningful information by partitioning into image regions. Image segmentation is a technique to locate certain objects or boundaries within an image.
by Rustam Stolkin - InTech
This book reports recent advances in the use of pattern recognition techniques for computer and robot vision. The areas of low level vision such as segmentation, edge detection, and region identification, are the focus of this book.