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Preface xviii

• We have written a comprehensive treatment of the modern features, par-
ticularly HOG and SIFT (both in Chapter 5), that drive applications ranging
from building image mosaics to object recognition.
• We give a detailed treatment of modern image editing techniques,in-
cluding removing shadows (Section 3.5), filling holes in images (Section 6.3),
noise removal (Section 6.4), and interactive image segmentation (Section 9.2).
• We give a comprehensive treatment of modern object recognition tech-
niques. We start with a practical discussion of classifiers (Chapter 15); we
then describe standard methods for image classification techniques (Chapter
16), and object detection (Chapter 17). Finally, Chapter 18 reviews a wide
range of recent topics in object recognition.

• Finally, this book has a very detailed index, and a bibliography that is as
comprehensive and up-to-date as we could make it.

WHY STUDY VISION?

Computer vision’s great trick is extracting descriptions of the world from pictures
or sequences of pictures. This is unequivocally useful. Taking pictures is usually
nondestructive and sometimes discreet. It is also easy and (now) cheap. The de-
scriptions that users seek can differ widely between applications. For example, a
technique known as structure from motion makes it possible to extract a representa-
tion of what is depicted and how the camera moved from a series of pictures. People
in the entertainment industry use these techniques to build three-dimensional (3D)
computer models of buildings, typically keeping the structure and throwing away
the motion. These models are used where real buildings cannot be; they are set fire
to, blown up, etc. Good, simple, accurate, and convincing models can be built from
quite small sets of photographs. People who wish to control mobile robots usually
keep the motion and throw away the structure. This is because they generally know
something about the area where the robot is working, but usually don’t know the
precise robot location in that area. They can determine it from information about
how a camera bolted to the robot is moving.
There are a number of other, important applications of computer vision. One
is in medical imaging: one builds software systems that can enhance imagery, or
identify important phenomena or events, or visualize information obtained by imag-
ing. Another is in inspection: one takes pictures of objects to determine whether
they are within specification. A third is in interpreting satellite images, both for
military purposes (a program might be required to determine what militarily inter-
esting phenomena have occurred in a given region recently; or what damage was
caused by a bombing) and for civilian purposes (what will this year’s maize crop
be? How much rainforest is left?) A fourth is in organizing and structuring collec-
tions of pictures. We know how to search and browse text libraries (though this is
a subject that still has difficult open questions) but don’t really know what to do
with image or video libraries.
Computer vision is at an extraordinary point in its development. The subject
itself has been around since the 1960s, but only recently has it been possible to
build useful computer systems using ideas from computer vision. This flourishing