CS294-6: Theoretical, Conceptual and Experimental Vision

Time: Mon & Wed, 13:10-14:30. Fall, 2006.
Location: 405 Soda Hall.
Units: 3.
Instructors: Ruzena Bajcsy, Shankar Sastry, and Allen Yang.
Emails: { bajcsy, sastry, yang } @ eecs
Office hour:     Mon from 2:30-3:30 after class or by appointment for Prof. Bajcsy. 665 Soda Hall.
                        Tue & Thu from 3:00-4:00 or by appointment for Prof. Sastry. 284 HMMB.
                        Tue from 2:30-4:00 for Allen. 343 Cory Hall (TRUST center).


The objective of this course is to give an introduction  to some basic concepts and foundations of computer vision with emphasis on  geometric methods in vision such as Structure from Motion and Generalized Principal Component Analysis (GPCA).  We will discuss both concepts, theory and implementation and experimental aspects. An important aspect of the course will be the development of active vision, which consists of closing the  loop around the camera sensor. Scenarios that we will cover include mobile robots (both driving and flying), manipulating objects and vision-based communication between people and machines.

The aim of the course is to enable students to set up experimental stereo camera systems, evaluate their characteristics, and use the latest geometric methods to calibrate them and reconstruct the motion of single and multiple objects. 

Required Textbook:

An Invitation  to 3D Vision. By Yi Ma, Stefano Soatto, Jana Kosecka , S. Shankar Sastry. Springer Verlag, 2005.

Website for the book : http://vision.ucla.edu/MASKS/. Website for the sample codes: http://cs.gmu.edu/~kosecka/bookcode.html.

Grading Policy:

There will be approximately 5 problem sets (50% credit)  and a final project (50% credit) for the class,  Participation in the class is expected from the registered students. Homework is due at the beginning of the class on the due date.

Course Projects Final Reports:
(Thank you all for your slides!)

Course Progress:





Week 1

Introduction and image formation



Week 2

Optics, radiometry and error analysis

Optic and radiometry.lecture2;

Sensor errors.lecture3.

HW 1 issued

Week 3

Image primitives and correspondence

lecture 4


Week 4

Review of basic algebra and geometry

lecture 5, lecture 6

HW1 due Sep 20.
HW 2 issued.

Week 5

Two-view geometry

lecture 7, lecture 8, lecture 9.

HW2 due Sep 27.
HW3 issued.

Week 6

Camera calibration

lecture 10, lecture 11, lecture 12


Week 7

Multiple-view geometry
Structure from symmetry

lecture 13 & 14
lecture 16

HW 3 due Oct 11
HW 4 issued.
Matlab files: 1, 2.

Week 8

Active vision

lecture 15.

Mid-proposal due Oct 18

Week 9

Actuve vision

lecture 17.


Week 10

Visual feedback

lecture 18, lecture 19.

HW 4 due Nov 1

Week 11

Real-time vision

lecture 20. lecture 21

HW 5 issued.

Week 12

Brief project overview.
Step-by-step reconstruction (by Prof. Soatto)


Week 13

GPCA: Introduction
Visual Compass (by Prof. Kosecka)

lecture 23.

HW 5 due Nov 22

Week 14

GPCA: Algebra

lecture 25


Week 15

GPCA: Robust statistics

lecture 26


Additional Material

  1. Other textbooks closely related: Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman.
  2. Reading on affine local feature detection and matching: [Mikolajczyk, Lowe, Nister(pdf, ppt)].
  3. Reading on sensor errors: [Kamberova].
  4. Reading on feature tracking: [Shi, Tommasini].
  5. Reading on approximate camera models and reconstruction: Orthographic [Tomasi]; Paraperspective [Aloimonos, Poelman]; and Affine [Kanatani].
  6. Reading on robust vision techniques: [Torr, Steward].
  7. Reading on active vision: [Bajcsy, Krotkov, Krotkov, Madden, Anderson, Uhlin].
  8. A textbook draft on GPCA: see Prof. Ma's course website
  9. Reading on robust GPCA: [Yang, Yang]. See also http://perception.csl.uiuc.edu/gpca/