I was very pleased to be asked to write this foreword, having seen snapshots of the development of this book since its inception. I write this having just returned from BMVC 2011, where I found that others had seen draft copies, and where I heard comments like “What amazing figures!”, “It's so comprehensive!”, and “He's so Bayesian!”.

But I don't want you to read this book just because it has amazing figures and provides new insights into vision algorithms of every kind, or even because it's “Bayesian” (although more on that later). I want you to read it because it makes clear the most important distinction in computer vision research: the difference between “model” and “algorithm.” This is akin to the distinction that Marr made with his three-level computational theory, but Prince's two-level distinction is made beautifully clear by his use of the language of probability.

Why is this distinction so important? Well, let us look at one of the oldest and apparently easiest problems in vision: separating an image into “figure” and “ground.” It is still common to hear students new to vision address this problem just as the early vision researchers did, by reciting an algorithm: first I'll use PCA to find the dominant color axis, then I'll generate a grayscale image, then I'll threshold that at some value, then I'll clean up the holes using morphological operators.