Introduction to Vision and Robotics
In 2013/2014 The course will be taught in Semester 2
10 Minute Introduction
|Email: bboom AT inf DOT ed DOT ac DOT uk
||Email: mherrman AT inf DOT ed DOT ac DOT uk
|Office: G.12 Informatics Forum
||Office: 1.42 Informatics Forum
|Phone: 651 3446
||Phone: 651 7177
Lecturers: Bastiaan Boom & Michael Herrmann
Mondays 11:10am in Faculty Room South, David Hume Tower and Thursdays 11:10am in G.07 Meadows Lecture Theatre,
Medical School (week 1-5) and in Ground Floor Exam Hall, Adam House (week 6-11).
NOTE: the first lecture will be on 13th January, 2014.
Lecture slides and handouts
will be available here before the start of each lecture.
The content will be arranged somewhat differently than
in previous years.
This year the lectures will not be recorded on video. You can still find
audio and videos of previous IVR lectures:
: Simon Smith (artificialsimon AT ed DOT ac DOT uk) and Davide Modolo (D.Modolo AT sms DOT ed DOT ac DOT uk)
Supervised Lab Times:
From week 2, Monday and Thursday 14:10 to 15:00, and Wednesday 12:10 to 13:00 at IPAB Robot teaching lab, AT 3.01.
(check back for updates of the lab times)
- Matlab, webot, khepera introduction
- More matlab, webot, khepera skills
- Image processing skills
- Visual classification skills
- Real Khepera control
There will be two pieces of assessed coursework carrying equal weight (12.5% each)
- A vision assignment during weeks 4-7, due 4pm Thursday 6th March.
There will be assessed demonstrations of the assignment in the Robotics Lab from 10:00-16:00 on
Friday 7th March. Details are available here. Matlab code
to read and show this data is here. You will
need to download these four data sets: dataset 1
- A robotics assignment during weeks 8-10, with the assignment due 4pm Thursday 27th March. There will be
assessed demonstrations of the assignment in the Robotics Lab from 10:00-16:00 on Friday 28th of March.
Details are available here
and here are some files that can be used in webots.
The practicals are done in teams of two.
For each assignment a demonstration of the results will be required and a
single, joint report is to be submitted by one of the students in each team.
Maximising your coursework practical score.
All practicals are covered by the school policy on
students are advised to be fully aware of this when submitting practical work.
Here is a
The lecture and practical contents define the examinable material.
- 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. Covers some of IVR, AV materials, but maybe less than 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. 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.
- MATLAB code for flat part recognition
- Online computer vision resources at University of Edinburgh (and beyond)
- Some YouTube videos of robots in action!
This page is maintained by the course lecturer, Michael Herrmann,
, room IF 1.42, ext 517177.