C4B Computer VisionMichaelmas Term 2009

Andrew Zisserman

Lecture notes and more at:

http://www.robots.ox.ac.uk/~az/lectures/cv

Course summary

1. Introduction; imaging geometry; camera geometry

2. Salient feature detection – edges, lines, corners

3. Recovering 3D from two images 1: epipolar geometry

4. Recovering 3D from two images 2: stereo correspondence

5. Structure and motion 1: estimating the F matrix, RANSAC

6. Structure and motion 2: SIFT, estimating H, more than two views

7. Visual motion and tracking

8. Object detection and recognition

Structure and Motion

Reconstruct • Scene geometry

• Camera motion

Unknowncameraviewpoints

Structure and Motion from Discrete Views

• Introduction

• Computing the fundamental matrix, F, from corner correspondences

• Feature matching

• RANSAC

• Estimation

• Determining ego-motion from F

• SIFT for wide baseline matching

• Computing a homography, H, from corner correspondences

• More than two views

• Batch and sequential solutions

Example

Original sequence Camera track and 3Dpoints

Example: compute F from 8 point correspondences

Images from a parallel camera stereo rig – epipolar lines y = y/

2

2-2

-2

-4 4

x1x2

x3

2

2-2

-2

-4 4

x/2

x/3

x/1

just consider first three points

write f in matrix form as

1