Mathematical Imaging Group
PhD COURSE IN COMPUTER VISION
 Lecture notes:
F1,
F2,
F3,
F4,
F5,
F6,
F7,
F8,
F9,
 Laboratory material 1999:
Lab 1,
Lab 2,
Lab 3,
Lab 4,
Lab 5.
Necessary matlab libraries:
compvisdata,
compvisgeneral,
compvislabbar,
compvisnew.
 Exercises:
Exercises lectures 16,
Exercises lectures 78,
 Project suggestions,
more project information will be placed here soon.
 Examination information.
A PhD Course in Computer Vision was given in 1999.
The course was given during two sessions of three and two days each. The
first session during 57 May 1999 and the second during 1920 August 1999.
The course consists of 9 lectures (2x45 minutes each),
5 laboratory sessions, exercises/homework, and a small project.
The course is sponsored by the project
VISIT.
Projects in the Computer vision course.
There are four choices on projekts.
 Generation of VRML models from one image.
In this project we are going to use results from the VISIT project
View synthesis. Given one image of a piecewise planar scene.
Find the intersection lines between the planes in the image.
Use this to calculate a 3D reconstruction of the textured
planes and encode this information in a VRML model.
A suggestion on a
project plan in swedishis
available as well as Björn Johanssons
article on the subject.
The first part of the project is to acquire a good image
to work with. It is advised that you show this image to
Björn before you proceed. Contact him via email at
bjornj@maths.lth.se
when you have acquired an image.
 Implementation of line detection in images.
Automatic feature detectionin images is an important
and difficult problem. In simplified situations, for example
line detection with very distinct edges, the problem is
possible to solve. The goal of the project is to develop
matlab routine that use edge detection and line fitting
to automatically extract and find edges. One idea is to use
subpixel edge detection to find edges and their normal direction
(estimate the gradient direction). Group edge points that have similar
normal direction and lie on the same line. Fit a line to these edge
points. Return the line parameter. Try the routine on
different images. Try first with the plcsequence.
 RANSAC matching from points in two images.
The goal of the project is to develop a small system for
automatic corner detection and matching in two images.
Use Harris corner detector and then RANSAC (RANdom SAmpling Consensus)
to find corresponding corners. Try the procedure on image pairs
or on image sequences.
A short image sequence is available here.
Also are some reports from a
student project that did tracking without RANSAC.
A possible extension is to use correlation and corner detection
to do a rough tracking of points in the sequence. From these
tracks perform a random sampling (of say 8 tracks) and then
solve for the fundamental matrix and check how many of the
other tracks fulfill the fundamental matrix constraint well.
Care must be taken in how well the fundamental constraint
must be fulfilled for the remaining tracks.
 VRMLgeneration from the five images plc.
The goal of the project is to construct a VRML model
of the lines and the conics in the image sequence plc*.pgm used
in the laboratory sessions. Extract image features and find
the correspondences manually. Calculate first projective and then
Euclidean
structure and motion using the assumption of constant intrinsic parameters.
Find out not only the motion of the camera, the structure of the points
but also the structure of the scene lines and the conics.
Encode the structure information in a VRML document and
tell us by email where the final reconstruction is located on the web.
First session 57 may 1999.
Timetable:
Wed 5/5: 10.1512.00: Lecture 1. Room MH:333.
Wed 5/5: 13.1515.00: Lecture 2. Room MH:333.
Wed 5/5: 15.1517.00: Exercise/Lab session 1. Room MH:139.
Wed 5/5: Evening. Special arrangement.
Thu 6/5: 8.1510.00: Lecture 3. Room MH:333.
Thu 6/5: 10.1512.00: Exercise/Lab session 2. Room MH:140.
Thu 6/5: 13.1515.00: Lecture 4. Room MH:333.
Thu 6/5: Evening. Gettogethergathering (barbecue if wheather allows).
Fri 7/5: 8.1510.00: Lecture 5. Room MH:333.
Fri 7/5: 10.1512.00: Exercise/Lab session 3. Room MH:139.
Fri 7/5: 13.1515.00: Lecture 6. Room MH:333.
Second session 1920 august 1999.
Timetable:
Thu 19/8: 10.1512.00: Lecture 7. Room MH:333.
Thu 19/8: 13.1515.00: Exercise/Lab session 4. Room MH:139.
Thu 19/8: 15.1517.00: Lecture 8. Room MH:333.
Thu 19/8: Evening arrangement?.
Fri 20/8: 8.1510.00: Lecture 9. Room MH:333.
Fri 20/8: 10.1512.00: Exercise/Lab session 5. Room MH:139.
Fri 20/8: 13.1515.00: Evaluation, discussion, questions. Room MH:333.
Below you can find more information about the course.
Here is more information about the course:
Computer vision is a rapidly growing research crossdisciplinary area,
which has far
reaching applications within robotics, autonomous systems,
virtual/augmented reality, medicine, etc. The basic problem is to
calculate the threedimensional structure of an unknown scene and the
egomotion of the camera from image measurements only.
Tools like projective geometry, tensor analysis and advanced
linear algebra have proved to be invaluable in understanding the
geometry of vision. This course in
computer vision will focus on the geometrical aspects of multiple
projective transformation.
Course content
Imaging models. Projective geometry. Advanced linear
algebra. Tensor analysis. Viewing geometry of points, lines, conics
and other features. Absolute orientation. Review of lowlevel vision
and feature extraction. Registration. Matching. Tracking.
Multiple view geometry. Bundle
adjustment. Selfcalibration. Invariants. Recognition.
Laboratory groups.
Each group is responsible for the extraction of 15 points
and 15 lines in one image. Coordinate the work among your self
and then distribute the information to the other groups.
For store the information in a text file
p1 = [ ...
10.1 123.4 567.8 ; ...
12.3 45.6 78.9; ...
1 1 1 ;...
];
l1 = [...
10.1 123.4 567.8 ; ...
12.3 45.6 78.9; ...
1 1 1 ;...
];
so that it will be easy to read the data to matlab.
Use the variables p1 and l1 for the points and lines in image plc001.
Use the variables p2 and l2 for the points and lines in image plc002.
And similarly for plc003, plc004 and plc005.
The groups are as follows:
 Group 1, (plc001)
 Group 2, (plc002)
 Group 3, (plc003)
 Group 4, (plc004)
 Group 5, (plc005)
Other email lists
All participants
Kalle och Anders
Förslag på hotell:
Hotell Sparta (nära LTH) tfn 04619 16 00
Hotell Ahlström (i centrum, ca 15 min gångväg från LTH) tfn 046211 01 74
Fler hotell här
Det kommer också att anordnas aktiviteter för kursdeltagarna
under kvällarna.
Address: Mathematical Imaging Group, Centre for Mathematical Sciences, Lund university, Box 118 S221 00 LUND, SWEDEN.
Phone:+46 46 222 85 37, Fax:+46 46 222 40 10
Publisher: Kalle Åström, kalle@maths.lth.se
Email: kalle@maths.lth.se
Last updated:990118
