Page 26 -

Preface xxiv

TABLE 2: A one-semester introductory class in computer vision for seniors or first-year
graduate students in computer science, electrical engineering, or other engineering or
science disciplines.
Week Chapter Sections Key topics
1 1, 2 1.1, 2.1, 2.2.x pinhole cameras, pixel shading models,
one inference from shading example
2 3 3.1–3.5 human color perception, color physics, color spaces,
image color model
3 4 all linear filters
4 5 all building local features
5 6 6.1, 6.2 texture representations from filters,
from vector quantization
6 7 7.1, 7.2 binocular geometry, stereopsis
7 8 8.1 structure from motion with perspective cameras
8 9 9.1–9.3 segmentation ideas, applications,
segmentation by clustering pixels
9 10 10.1–10.4 Hough transform, fitting lines, robustness, RANSAC,
10 11 11.1-11.3 simple tracking strategies, tracking by matching,
Kalman filters, data association
11 12 all registration
12 15 all classification
13 16 all classifying images
14 17 all detection
15 choice all one of chapters 14, 19, 20, 21 (application topics)

SAMPLE SYLLABUSES
The whole book can be covered in two (rather intense) semesters, by starting at
the first page and plunging on. Ideally, one would cover one application chapter—
probably the chapter on image-based rendering—in the first semester, and the other
one in the second. Few departments will experience heavy demand for such a de-
tailed sequence of courses. We have tried to structure this book so that instructors
can choose areas according to taste. Sample syllabuses for busy 15-week semesters
appear in Tables 2 to 6, structured according to needs that can reasonably be ex-
pected. We would encourage (and expect!) instructors to rearrange these according
to taste.
Table 2 contains a suggested syllabus for a one-semester introductory class
in computer vision for seniors or first-year graduate students in computer science,
electrical engineering, or other engineering or science disciplines. The students
receive a broad presentation of the field, including application areas such as digital
libraries and image-based rendering. Although the hardest theoretical material is
omitted, there is a thorough treatment of the basic geometry and physics of image
formation. We assume that students will have a wide range of backgrounds, and
can be assigned background readings in probability. We have put off the application
chapters to the end, but many may prefer to cover them earlier.
Table 3 contains a syllabus for students of computer graphics who want to
know the elements of vision that are relevant to their topic. We have emphasized
methods that make it possible to recover object models from image information;