Computer Vision I

Sequoyah Hall, Room 148

Instructor: David Kriegman

Office: EBU3B, Room 4120

Phone: (858) 822-2424

Email: kriegman at cs.ucsd.edu

Office Hours: Wed. 1:00-2:30

TA: Neil Alldrin

Email: nalldrin at cs.ucsd.edu

Office Hours: Tue. 11:00-11:50, CSE B240A

Assignments: 60%

Final Exam: 40%

- Test Images: here
- Reference: "A Maximum Likelihood Stereo Algorithm", by Cox, Hingorani, Rao, and Maggs,

- Test Images: here
- References:

"Hierarchical Model-Based Motion Estimation", by Bergen et al., ECCV 1992

"Determining Optical Flow for Large Motions Using Parametric Models in a Hierarchical Framework", by Barron and Khurana

"The Computation of Optical Flow", by Beauchemin and Barron

Week | Date / Link to lecture notes | Topic / Readings |
---|---|---|

1 | Jan. 10 Linear algebra review Random variables review |
Intro to Computer Vision |

Jan. 12 | Human Visual System, F&P sec. 1.3 | |

2 | Jan. 17 | Rigid Transformatoins SE(3), SO(3), Camera & Lenses, F&P Sec. 2.1, F&P Chap. 1 |

Jan. 19 | Perspective, Affine, orthographic projection, F&P 2.2, 2.3 | |

3 |
Jan. 24 Partb |
Radiometry (Irradiance, Radiance, BRDF), F&P Chapter 4 |

Jan. 26 | Special BRDF's, Light Sources, Photometric Stereo, F&P, 5.2-5.4 | |

4 | Jan. 31 | Photometric stereo |

Feb. 2 Lec8b-col |
Illumination Cones, Belhumeur, Kriegman, What Is the Set of Images of an Object under All Possible Illumination Conditions?, IJCV 28(3), 1998, 245-260 | |

5 | Feb. 7 Part b |
Color; F&P, Chap. 6 Some on-line sources include: Basics of Color, A FAQ on Color |

Feb. 9 | Color, Dichromatic model | |

6 |
Feb. 14 |
Filtering F&P Chap. 7, 8 |

Feb. 16 | Edges | |

7 | Feb. 21 | Epipolar Constraint and Stereo I, F&P Sec. 10.1Stereo II, Dynamic Programming, Chapter 11 |

Feb. 23 | Optical Flow, Trucco and Verri, pp. 178-194 (note: link only works through campus network) | |

8 | Feb. 28 | Infinitesimal structure from Motion, Trucco and Verri pp. 195-202, 208-211 |

Mar. 2 | Tracking, F&P Chap.17 | |

9 | Mar. 7 | Statistical Pattern Recognition, F&P 22.1-22.3 |

Mar. 9 | Support Vector Machines & Kernel Methods, F&P Sec. 22.5, 22.8 | |

10 | Mar. 14 | Appearance-based Recognition, Eigenface, Fisherface, Appearance Manifolds |

Mar. 16 | Model-based recognition, F&P Chap. 18 |

The primary language will be Matlab. . Click here for Serge Belongie's Matlab resource links.

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)

Camera Calibration Toolbox for Matlab (Bouguet)

Microsoft Camera Calibration Code (Zhang)

Intel OpenCV

CVonline

The Computer Vision Home Page

Handy Math reference: MathWorld

Related classes at UCSD: CSE 190-B, CSE 166, CSE 252B

Some other useful vision links