Introduction to Vision and Robotics
Course Descriptor
10 Minute Introduction
Course Organisers:
| Bob Fisher |
Michael Herrmann |
| Email: rbf@inf.ed.ac.uk |
Email: mherrman@inf.ed.ac.uk |
| Office: 1.26 Informatics Forum |
Office: 1.42 Informatics Forum |
| Phone: 651 3441 |
Phone: 651 7177 |
Lectures:
Lecturer: Bob Fisher & Michael Herrmann
Lecture times:
Tuesdays 9:00am in DHT LTA and Fridays 9:00am in AT LT1.
NOTE 1: the first lecture is Friday September 23, 2011.
NOTE 2: we had hoped to offer IVR in both semesters, but
it is only possible to offer it in semester 1 this year.
Lecture topics and handouts: here.
Audio and videos of lectures:
2007/8,
2008/9,
2010/11
and
2011/12.
Because of equipment technical problems, we are not always able to capture all lectures,
so lectures from previous years might help.
Tutorial/Practicals:
Demonstrators:
Vladimir Ivan,
Simon Smith,
Efstathios Vafeias
Supervised Lab Times:
From week 2, Tuesday 10:00, 11:10, 16:00, Friday 10:00, 11:10, 12:10 at IPAB Robot teaching lab, AT 3.01.
Practical notes (Do not use the old version at the machines in the robotics lab):
Week 2 (new) - Matlab, webot, khepera introduction
(
old version)
Week 3 (new) - More matlab, webot, khepera skills
(
old version)
Week 4 - Image processing skills
Week 5 - Visual classification skills
Week 6 - Real Khepera control
Assessment:
Coursework (25%)
There will be two pieces of assessed coursework
- A vision assignment during weeks 5-7, with the assignment due 4pm Thursday November 3. There will be
assessed demonstrations of the assignment in the Robotics Lab from 10:00-16:00 on Friday Nov 4.
Details available here.
Practical dataset 1 and
dataset 2.
- A robotics assignment during weeks 8-10, with the assignment due 4pm Thursday November 24. There will be
assessed demonstrations of the assignment in the Robotics Lab from 10:00-16:00 on Friday Nov 25.
Details are available here anda
here is a tar archive of files that can be used in webots.
The practicals are done in teams of two.
A single, joint, report is to be submitted by one student.
Maximising your coursework practical score.
All practicals are covered by the school policy on
plagiarism and
students are advised to be fully aware of this when submitting practical work.
Exam (75%)
Here is a
sample paper
with
sample answers
The lecture and practical contents define the examinable material.
Reading list:
- Russell & Norvig Chapters 24 & 25 in Artificial
Intelligence: A modern approach, Prentice Hall, 1995, ISBN 0130803022. Highly recommended
- Solomon & Breckon, "Fundamentals of Digital Image Processing - A Practical Approach with
Examples in Matlab", Wiley-Blackwell, 2010, ISBN: 978-0470844731.Highly recommended
- Robin R. Murphy, Introduction to AI Robotics, MIT Press,
2000, ISBN 0262133830.Recommended, supplementary for robotics
- W. Burger, M. Burge; Principles of Digital Image Processing, Springer, 2009, ISBN: 978-1-84800-190-9.£22 - covers some of IVR, AV materials, but maybe < 50%. Also online free inside the Univ here.
- R.C. Gonzalez, R.E. Woods, S.L. Eddins; Digital Image Processing Using MATLAB, 2nd edition, Prentice Hall, 2009, ISBN 9780982085400.$140 - Excellent but expensive book, covers a lot of IVR, some of AV.
Also a book support site here.
- Introduction to Machine Learning by Ethem Alpaydin, The MIT Press, October 2004, ISBN 0-262-01211-1.
Recommended. Chapters 1-5 are a deeper exploration of the Bayesian classification topic
- Phillip J. McKerrow, Introduction to Robotics, Addison Wesley,
1998, ISBN 0 201 18240 8 (now out of print, but some copies can be found on amazon).Supplementary
- Ulrich Nehmzow, Mobile Robotics: A Practical Introduction,
Springer; 2nd ed. edition (8 July 2003). Recommended.
See individual lecture handouts for further reference material.
Resources
- MATLAB code for lectures 3-6 (flat part recognition)
- Online computer vision resources at University of Edinburgh (and beyond)
- Some YouTube videos of robots in action!
Communications:
This page is maintained by the course lecturer, Bob Fisher,
rbf@inf.ed.ac.uk, room IF 1.26, ext 513441.
