• Title: Computer Vision: Algorithms and Applications
• Author(s) Richard Szeliski
• Publisher: Springer; 2011th edition (October 19, 2010); eBook (Draft, September 3, 2010)
• Permission: This electronic draft is for non-commercial personal use only, and may not be posted or re-distributed in any form.
• Hardcover: 832 pages
• eBook: PDF (979 pages, 22.1 MB)
• Language: English, Chinese, and Japanese
• ISBN-10: 1848829345
• ISBN-13: 978-1848829343
• Share This:  

Book Description

As humans, we perceive the three-dimensional structure of the world around us with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem, and what is the current state of the art?

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging and fun consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

More than just a source of "recipes", this text/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting this process to produce the best possible descriptions of a scene. Exercises are presented throughout the book, with a heavy emphasis on testing algorithms.

Suitable for either an undergraduate or a graduate-level course in computer vision, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries.

• Dr. Richard Szeliski has over twenty years' experience in computer vision research, most notably at Digital Equipment Corporation and Microsoft.

• Amazon

• Computer Vision, Machine Vision, and Deep Learning
• Deep Learning and Neural Networks
• Digital Signal Processing (DSP), Sound and Imaging Processing
• Computer Graphics and Digital Images
• Artificial Intelligence, Machine Learning, and Logic Programming

• Computer Vision: Algorithms and Applications (Richard Szeliski)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF
• The Mirror Site (3) - PDF

• 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.

• Computer Vision: Models, Learning, and Inference (Simon Prince)

  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. The detailed methodological presentation is useful for practitioners of computer vision.

• 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.

• Modern Robotics with OpenCV (Widodo Budiharto)

  This book is written to provide an introduction to intelligent robotics using OpenCV. It gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated Robotics applications.

• Multimedia Forensics (Husrev Taha Sencar, et al)

  Media forensics has never been more relevant to societal life. This book presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics.

• 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 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.

• Numerical Algorithms: Computer Vision, Machine Learning, etc.

  This book presents a new approach to numerical analysis for modern computer scientists, covers a wide range of topics - from numerical linear algebra to optimization and differential equations - focusing on real-world motivation and unifying themes.

• Deep Learning and Neural Networks

  A collection of books related to Deep Learning and Neural Networks, including (but not limited to): Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN, or ConvNet), GANs, etc.