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1.1 What is computer vision? 7

• Stitching: turning overlapping photos into a single seamlessly stitched panorama (Fig-
ure 1.5a), as described in Chapter 9;

• Exposure bracketing: merging multiple exposures taken under challenging lighting
conditions (strong sunlight and shadows) into a single perfectly exposed image (Fig-
ure 1.5b), as described in Section 10.2;
• Morphing: turning a picture of one of your friends into another, using a seamless
morph transition (Figure 1.5c);

• 3D modeling: converting one or more snapshots into a 3D model of the object or
person you are photographing (Figure 1.5d), as described in Section 12.6

• Video match move and stabilization: inserting 2D pictures or 3D models into your
3
videos by automatically tracking nearby reference points (see Section 7.4.2) or using
motion estimates to remove shake from your videos (see Section 8.2.1);
• Photo-based walkthroughs: navigating a large collection of photographs, such as the
interior of your house, by flying between different photos in 3D (see Sections 13.1.2
and 13.5.5)

• Face detection: for improved camera focusing as well as more relevant image search-
ing (see Section 14.1.1);
• Visual authentication: automatically logging family members onto your home com-
puter as they sit down in front of the webcam (see Section 14.2).

The great thing about these applications is that they are already familiar to most students;
they are, at least, technologies that students can immediately appreciate and use with their
own personal media. Since computer vision is a challenging topic, given the wide range
4
of mathematics being covered and the intrinsically difficult nature of the problems being
solved, having fun and relevant problems to work on can be highly motivating and inspiring.
The other major reason why this book has a strong focus on applications is that they can
be used to formulate and constrain the potentially open-ended problems endemic in vision.
For example, if someone comes to me and asks for a good edge detector, my first question is
usually to ask why? What kind of problem are they trying to solve and why do they believe
that edge detection is an important component? If they are trying to locate faces, I usually
point out that most successful face detectors use a combination of skin color detection (Exer-
cise 2.8) and simple blob features Section 14.1.1; they do not rely on edge detection. If they
are trying to match door and window edges in a building for the purpose of 3D reconstruction,
I tell them that edges are a fine idea but it is better to tune the edge detector for long edges
(see Sections 3.2.3 and 4.2) and link them together into straight lines with common vanishing
points before matching (see Section 4.3).
Thus, it is better to think back from the problem at hand to suitable techniques, rather
than to grab the first technique that you may have heard of. This kind of working back from
3
For a fun student project on this topic, see the “PhotoBook” project at http://www.cc.gatech.edu/dvfx/videos/
dvfx2005.html.
4 These techniques include physics, Euclidean and projective geometry, statistics, and optimization. They make
computer vision a fascinating field to study and a great way to learn techniques widely applicable in other fields.