xvi Computer Vision: Algorithms and Applications (March 30, 2008 AM draft)
areas), and as a general reference to the fundamental techniques and most recent research literature
in computer vision. To this end, I have attempted wherever possible to at least cite the newest
research in each sub-field, even if the technical details are too complex to cover in the book itself.
In teaching our courses, we have found it useful for the students to attempt a number of small
implementation projects, which often build on one another, in order to get them used to working
with real-world images and the challenges that these present. The students are then asked to choose
an individual topic for each of their small group final projects. (Sometimes these projects even turn
into conference papers!) The exercises at the end of each chapter contain numerous suggestions
for both smaller mid-term projects, as well as more open-ended problems whose solution is still
an active research area. Wherever possible, I encourage students to try their algorithms on their
own personal photographs, since this better motivates them, often leads to creative variants on the
problems, and better acquaints them with the variety and complexity of real-world imagery.
In formulating and solving computer vision problems, I have often found it useful to draw
inspiration from three different high-level approaches:
1. Scientific: build detailed models of the phenomena that created the images under consider-
ation and develop mathematical techniques to invert these processes in order to recover the
quantities of interest (where necessary, making simplifying assumption to make the mathe-
matics more tractable).
2. Statistical: use probabilistic models to quantify the (prior) likelihood of your unknowns and
the noisy measurement processes that produce the input images, then infer the best possible
estimates of your desired quantities and analyze their resulting uncertainties. The inference
algorithms used are often closely related to the optimization techniques used to invert the
(scientific) image formation processes.
3. Engineering: develop techniques that are simple to describe and implement, but that are
also known to work well in practice. Test these techniques to understand their limitation and
failure modes, as well as their expected computational costs (run-time performance).
All three approaches are used in the book, and they all build on each other.
My personal research and development philosophy (and hence the exercises in the book) have
a strong emphasis on testing algorithms. It’s too easy in computer vision to develop an algorithm
that does something plausible on a few images rather than something correct. The best way to
validate your algorithms is to use a three part strategy.
First, test your algorithm on clean synthetic data, for which the exact results are known. Sec-
ond, add noise to data, and evaluate how the performance degrades as a function of noise level.
Finally, test the algorithm on real-world data, preferably drawn from a widely variable source,
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