Course Information:
Instructor: David
Kriegman
Office: EBU3B, Room 4120
Phone: (858) 822-2424
Email: kriegman at cs.ucsd.edu
Office Hours: Tuesday 4:30 – 5:30pm
TA: Oscar Beijbom
Email: obeijbom at ucsd.edu
Office: EBU-3b Room B275
Office Hours: Wednesday: 12-1pm
Class Description:
Comprehensive introduction to computer vision providing broad coverage
including low level vision (image formation, photometry, color, image feature
detection), inferring 3D properties from images (shape-from-shading, stereo
vision, motion interpretation) and object recognition. A companion course,
CSE252B, Computer Vision II is taught in the spring quarter. 4 units.
Required
Text:
"Computer vision: A Modern Approach," (2nd ed.) David
A. Forsyth, Jean
Ponce, Prentice Hall, ISBN: 013608592X
Supplemental Text:
“Computer Vision: Algorithms and Applications”, Richard Szeliski, is available at: http://szeliski.org/Book/.
Prerequisites:
Linear algebra and Multivariable calculus (e.g., Math 20A & 20F),
programming, data structure/algorithms (e.g., CSE100). Probability can also be
useful.
Programming:
Assignments will include both written problem sets and programming assignments
in Matlab. Students can either purchase the Matlab student edition or use
copies available on University machines such as are available in the
APE Lab.
Grading:
Assignments: 60%
Final Exam: 40%
Late Policy:
Written homework will be due in class and accepted thereafter with a penalty of
10% per day starting from the due date. Programming assignments will
have a hand-in procedure described with the assignment, and also has a 10% per
day late penalty. No assignments will be accepted after the graded assignments
have been returned or the solutions have been released.
Collaboration Policy:
You may work together on homework assignments to discuss ideas and methods only, however what you turn in should be your own work and any code should be your own coding. Copying is not permitted.
Assignments
Homework 0 [Due 10/3]: Getting Started with Matlab. Updated text to appread on Thursay Sep. 26.
Homework 1 [Due 10/17]: Camera Models and Homography
Homework 2 [Due 10/31]. New due date: [11/05].
Homework 3 [Due 11/19] New due date: [11/21].
Homework 4 [Due 12/06]
Syllabus:
[Note that this Syllabus is
tentative & subject to change ]
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Date / Link to lecture notes
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Topic / Readings
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Sep. 26
Linear
algebra review
Random
variables review
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Intro to Computer Vision
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Oct. 1
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Human Visual System, F&P sec. 1.3. RS 1-18
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Oct. 3
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Image Formation
and Cameras. Projective Geometry. Homogenous Coordinates. Homography
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Oct. 8
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Homography. Perspective, Affine, orthographic projection and geometry. Camera Models. Lenses
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Oct. 10
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Lenses Continued. SO(3) Transformations. Radiometry (Irradiance, Radiance, BRDF), F&P Chapter
4
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Oct. 15
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Radiometry Continued. Radiance and Irradiance. Special BRDF's, Light Sources, Photometric Stereo.
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Oct. 17
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Lighting and Photometric Stereo F&P Section 2.2
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Oct. 22
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Photometric Stereo F&P Section 2.2
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Oct. 24
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Illumination Cones, Belhumeur, Kriegman, What Is the Set
of Images of an Object under All Possible Illumination Conditions?, IJCV
28(3), 1998, 245-260
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Oct. 29
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Color, Dichromatic model, RS 67
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Oct. 31
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Color, Dichromatic Model Continued. SUV Space. Filtering F&P Chap. 7, 8, RS. 101-1.22
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Nov. 5
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Edges RS 238=249
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Nov. 7
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Epipolar Constraint and Stereo I, F&P Sec. 10.1, RS
530-544
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Nov. 12
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Stereo II, Dynamic Programming, Chapter 11, 545-548,
552-556
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Nov. 14
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Optical Flow, Trucco
and Verri, pp. 178-194, RS 4381-414
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Nov. 19
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Infinitesimal structure from Motion, Trucco
and Verri pp. 195-202, 208-211
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Nov. 21
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Tracking, F&P Chap.17, RS 235-237, 282-284, 551-552, 605-609
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Nov. 26
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Statistical Pattern Recognition, F&P 22.1-22.3
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Dec. 3
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Support Vector Machines & Kernel Methods, F&P Sec.
22.5, 22.8
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Dec. 5
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Appearance-based Recognition and Model-based recognition, F&P Chap. 18, RS 655-722
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Notes and Links:
Programming languages:
The primary language will be Matlab. Click here for Serge
Belongie's Matlab resource links.
Other
excellent textbooks:
Introductory
Techniques for 3-D Computer Vision, Trucco and Verri (textbook for CSE152)
An Invitation to 3D Vision: From Images
to Geometric Models, Ma, Soatto, Kosecka and Sastry, Springer Verlag, 2003,
ISBN 0-387-00893-4 (textbook for CSE252B)
Some useful
links:
Camera Calibration
Toolbox for Matlab (Bouguet)
Microsoft Camera
Calibration Code (Zhang)
Intel OpenCV
CVonline
The Computer Vision Home
Page
Handy Math reference: MathWorld
