This is a first course in Computer Vision. It is intended for seniors or first-level graduate students in math-intensive curricula, such as electrical engineering, computer engineering, math, physics, or computer science.

While this book provides a “foundation” in computer vision, a foundation in universitylevel mathematics is required for this book. We suggest that all students have the usual three-semester course in differential calculus, at least one course in differential equations, and one or two courses in concepts of matrices and linear algebra.

The authors feel strongly that a student in Computer Vision should have a reasonably good grasp of what the computer is actually doing – and that means programming. While MATLAB is a fantastic research tool, and provides wonderful rendering tools, using MATLAB may prevent the student from gaining this insight into the basic operations (unless, of course, the student actually writes MATLAB code and doesn't use a toolbox). We prefer the student learn and use C or C++.

We have a software package available with which we have had considerable success, described in Appendix C. It can be downloaded for free for MacOSX64, Linux64, and most versions of Windows, along with a lot of images, from www.cambridge.org/ 9781107184886. This package is, however, not required to use the book. The instructor may choose any software platform, including MATLAB.

One style of teaching this course, the one we usually follow, is to have a number of small projects, roughly a week or two in duration, instead of a single end-of-class project.

In teaching this course, the principal problems we have encountered are with students who don't know the difference between a compiler and a fishing rod, and students who think an eigenvalue is made with sugar and served with syrup. The emphasis on knowing linear algebra is very important.

We also recommend that prior to taking this course, students take a class in image processing, in which they learn what a pixel is, what restoration does, and the difference between enhancement and filtering.