• Title: Computer Vision: Models, Learning, and Inference
• Author(s) Simon J. D. Prince
• Publisher: Cambridge University Press; 1 edition (2012); eBook (Draft, 2012)
• Permission: You may take one copy of the book draft for personal use but not for distribution.
• Hardcover: 632 pages
• eBook: PDF (667 pages, 116 MB)
• Language: English
• ISBN-10: 1107011795
• ISBN-13:978-1107011793
• Share This:  

Book Description

With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision.

• Dr. Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London.

• Amazon

• Computer and Machine Vision
• Artificial Intelligence, Machine Learning, and Logic Programming
• Probability, Stochastic Process, Queueing Theory, etc.
• Digital Signal Processing (DSP), Sound and Imaging Processing
• Human-Computer Interaction

• Computer Vision: Models, Learning, and Inference (Simon J. D. Prince)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF
• An Implementation of Algorithms of the Book using Python

• Understanding Deep Learning (Simon J.D. Prince)

  An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Only the most important ideas to provide a high density of critical information in an intuitive and digestible form.

• Distant Viewing: Computational Exploration of Digital Images

  This book presents a new theory and methodology for the application of computer vision methods to the computational analysis of collected, digitized visual materials, called “distant viewing”.

• Computer Vision: Algorithms and Applications, Second Edition

  This book explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications as well as for fun, consumer-level tasks.

• Programming Computer Vision with Python: Tools and Algorithms

  This book is a hands-on introduction to computer vision using Python. It gives 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.

• Developing Graphics Frameworks with Python and OpenGL

  It shows you how to create software for rendering complete three-dimensional scenes, explains the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive worlds.

• Computer Vision Metrics: Survey, Taxonomy, and Analysis

  This book provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features.

• Mastering OpenCV 4: Computer Vision & Image Processing Apps

  This book targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, it delivers complete projects from ideation to running code, targeting current hot topics, etc.

• Computer Vision (Dana H. Ballard, et al)

  This book is the construction of explicit, meaningful descriptions of physical objects from images. Image understanding is very different from image processing, which studies image-to-image transformations, not explicit description building.

• Computer Vision (Xiong Zhihui)

  This book presents computer vision on application of robotics, and on advanced approachs for computer vision (such as omnidirectional vision), research on RFID technology integrating stereo vision to localize an indoor mobile robot, etc.