Books for computer vision

Although almost anything could be found online, including everything about computer vision, a single coherent source is nice to have. A nice book is such a source :). Below are some of the books that are usually recommended, and quite famous, for various areas of computer vision.

1. Introduction and basics: "Computer Vision: A modern approach" - David A. Forsyth and Jean Ponce
2. Geometry: "Multiple view geometry in computer vision" - Richard Hartley & Andrew Zisserman
3. Geometry: "Three-Dimensional computer vision" - Olivier Faugeras
4. Reference: "Computer Vision: Algorithms and Applications" - Richard Szeliski (Online free version available on website)

Since we often resort to machine learning tools for vision problems, it would be unfair to not recommend some machine learning related books

1. Good reference: "Pattern Recognition and machine learning" - Christopher M. Bishop
2. Great textbook: "Machine Learning" - Tom M. Mitchell
3. Graphical Models: "Probabilistic Graphical Models: Principles and Techniques" - Daphne Koller and Nir Friedman 

More often than not, we will end up in situations that require solving complex mathematical problems. One common case is optimizing some cost function to determine optimal parameter values. Although, we generally resort to using some tool box for such situations (e.g. matlab's "fminsearch()"), following are some books that do a great job at explaining the machinery behind these tools

1. Optimization: "Convex Optimization" - Stephen Boyd and Lieven Vandenberghe (Online free version)