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[1]
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R. Hartley and A. Zisserman.
Multiple View Goemetry in Computer Vision.
Cambridge University Press, 2nd edition, 2004.
Probably the best known text on 3D vision. I haven't looked in
the second edition but the earlier edition had good descriptions
of the epipolar geometry that is used in the course. There is much
more in this book than just the basic 2 view geometry.
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[2]
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L. Shapiro and G. Stockman.
Computer Vision.
Prentice-Hall, 2001.
Looks like this would be a pretty good reference. I haven't read
into the book however, and I dont know how well things are
covered. It should cover much of the 2D aspect of the course and
also a reasonable covereage of the 3D material.
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[3]
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W.E. Snyder and H. Qi.
Machine Vision.
Cambridge University Press, 2004.
This book is a new book that I haven't had a chance to read yet.
It focuses almost exclusively on 2D vision and probabilistic
classification. There are good sections on linear filters,
morphology, Segmentation, Hough transforms, template matchin and a
nice introduction to neural networks. This book aligns quite
nicely with the 2D part of the course
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[4]
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B. Jähne.
Digital Image Processing.
Springer, 5th, revised and extended edition, 2002.
This book is a recent edition of a classic in the field. It has a
theoretical flavour for 2D image processing. It provides good
theoretical developments of tools such a 2D fourier theory,
probability theory, etc, in the context of image processing. It
has some useful info about sampling. There is a good section on
edge detection and motion identification and some reasonable
material on segmentation, morphology and classification. It
doesn't fit particularly well with the structure of the course.
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[5]
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D. Forsyth and J. Ponce.
Computer Vision; A modern approach.
Prentice Hall, 2003.
This book provides a moderate coverage of issues in computer
vision with a strong focus on 3-D vision. It has a poor coverege
of 2D vision techniques, covering only linear filtering techniques
and colour. It has a reasonable development of epipolar geometry
and 3D vision, although it is not the best written I have seen.
There is a nice section on segmentation and probabilistic
classification. It then has a wide range of topics covered in some
detail on contemporary research issues. Generally a good book but
not very well aligned with the course.
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[6]
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Y. Ma, S. Soatto, J. Kosecka, and S. Sastry.
An invitation to 3-D vision; From images to geometric models.
Interdisciplinary applied mathematics. Spinger, 2004.
This book is concerned primarily with the geometry of vision. It
contains no detail on 2-D image processing or probabilistic classification.
It looks well written but pitched at an advanced level. Good long
term investment for people looking to do further work in Computer
Vision.
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