CSE 252A: Computer Vision I (original) (raw)
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 theAPE 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.
- Matlab Tutorial Code Snippets
- trees.tif photo.jpg
- border1.jpeg center1.jpeg border2.jpeg center2.jpeg
- border1.png center1.png border2.png center2.png
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 ]
| | Date / Link to lecture notes | Topic / Readings | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | Sep. 26 Linear algebra review Random variables review | Intro to Computer Vision | | | Oct. 1 | Human Visual System, F&P sec. 1.3. RS 1-18 | | Oct. 3 | Image Formation and Cameras. Projective Geometry. Homogenous Coordinates. Homography | | | Oct. 8 | Homography. Perspective, Affine, orthographic projection and geometry. Camera Models. Lenses | | Oct. 10 | Lenses Continued. SO(3) Transformations. Radiometry (Irradiance, Radiance, BRDF), F&P Chapter 4 | | | Oct. 15 | Radiometry Continued. Radiance and Irradiance. Special BRDF's, Light Sources, Photometric Stereo. | | Oct. 17 | Lighting and Photometric Stereo F&P Section 2.2 | | | Oct. 22 | Photometric Stereo F&P Section 2.2 | | Oct. 24 | Illumination Cones, Belhumeur, Kriegman, What Is the Set of Images of an Object under All Possible Illumination Conditions?, IJCV 28(3), 1998, 245-260 | | | Oct. 29 | Color, Dichromatic model, RS 67 | | Oct. 31 | Color, Dichromatic Model Continued. SUV Space. Filtering F&P Chap. 7, 8, RS. 101-1.22 | | | Nov. 5 | Edges RS 238=249 | | Nov. 7 | Epipolar Constraint and Stereo I, F&P Sec. 10.1, RS 530-544 | | | Nov. 12 | Stereo II, Dynamic Programming, Chapter 11, 545-548, 552-556 | | Nov. 14 | Optical Flow, Trucco and Verri, pp. 178-194, RS 4381-414 | | | Nov. 19 | Infinitesimal structure from Motion, Trucco and Verri pp. 195-202, 208-211 | | Nov. 21 | Tracking, F&P Chap.17, RS 235-237, 282-284, 551-552, 605-609 | | | Nov. 26 | Statistical Pattern Recognition, F&P 22.1-22.3 | | | Dec. 3 | Support Vector Machines & Kernel Methods, F&P Sec. 22.5, 22.8 | | Dec. 5 | Appearance-based Recognition and Model-based recognition, F&P Chap. 18, RS 655-722 |