10 Minute Introduction

Bob Fisher | Michael Herrmann |

Email: rbf AT inf DOT ed DOT ac DOT uk | Email: mherrman AT inf DOT ed DOT ac DOT uk |

Office: 1.26 Informatics Forum | Office: 1.42 Informatics Forum |

Phone: 651 3441 | Phone: 651 7177 |

Lecturers: Bob Fisher & Michael Herrmann

Lecture times:
Mondays 11:10am and Thursdays 11:10am in Appleton Tower Lecture Theatre 1

**NOTE: the first lecture will be on 12th January, 2015.**

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.

IMPORTANT INFORMATION: This course is not taught by the traditional lectures. Instead, we use an Inverted Classroom method. This means that you will have about 8 hours of video to watch in your own time. This material is assessible. There are still 2 full class meetings each week. At each meeting we will be discussing any questions that you either suggest in advance or raise in class on the day. There will not be a 'lecture'.

Here is the link to the vision lecture videos, associated readings and associated Matlab. Here is an Introduction to how the course is intended to be followed.

This year the robotics lectures will not be recorded on video.

You may be able to find audio and videos of previous IVR lectures: 2010/11.

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.

Instructions:

Week 2 - Matlab, webot, khepera introduction

Week 3 - More matlab, webot, khepera skills

Week 4 - Image processing skills

Week 5 - Visual classification skills

No practicals in week 6.

Week 7 - Real Khepera control

Coursework (25%)

**There will be two pieces of assessed coursework carrying equal weight** (12.5% each)

- A vision assignment during weeks 4-6, announced by Thursday February 12 and due 4pm Thursday February 26.
Marked results will be returned by March 12.
There will be an assessed demonstration of the assignment in the Robotics Lab from 9:00-13:00 on
Friday February 27. Details are available here.
You will
need to download these two data sets:
dataset 1,
dataset 2.
**Feedback**: You will get oral feedback at the demonstration and a written report on the marked submission. The written feedback will cover specific issues about the assignment as well as a set of marks for the different components of the assignment. General observations about the solutions from all of the submitted practicals will be circulated by email.

- A robotics assignment during weeks 7-10, with the assignment due 4pm Thursday 26th March. There will be
assessed demonstrations of the assignment in the Robotics Lab from 10:00-16:00 on Friday 27th of March.
Details are available here. More information is here,
and here are some files that can be used in webots.
**Feedback**will be given in the same way as for the first assignment.

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
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.

**Formative feedback**: You can get formative feedback in several ways:
1) by asking the lab demonstrator questions in the supervised lab sessions,
2) by asking the lecturer questions during (oral) and after class (oral,email),
3) by asking your fellow students using the wiki,
4) by asking your practical partner,
5) the drill questions given after each videoed lecture segment (vision course only), and
6) the general comments on the class's approaches to the practical solutions emailed after the
marking is complete.

**Sumative feedback**: You can get sumative feedback in several ways:
1) oral feedback during the demonstrations of your practicals, and
2) written marks and comments on your submitted practical assignments.

- 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", "Book Support Site", 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**.

- MATLAB code for flat part recognition
- Online computer vision resources at University of Edinburgh (and beyond)
- An index to online notes on some topics covered in the lectures.
- Online computer vision books.
- The HIPR image processing summary and on-line interactive demonstration package. Direct access is via HIPR2
- CVonline - an online encyclopedia of computer vision. (About 1200 of the 1600 topics have entries so far.)
- Illustrated Dictionary of Computer Vision (UoE internal access only)
- Online computer vision lectures from the University of Central Florida.
- Online documentation for the matlab image processing toolkit.
- Recent academic and industrial research in computer vision/graphics:
- ACM SIGGRAPH (graphics)
- Eurographics Digital Library (graphics)
- USC Bibliography (computer vision)

- Online JAVA robot navigation demo
- Tutorial notes on probabilistic robotics with some nice examples related to the course.

- Some YouTube videos of robots in action!

- Course Discussion Forum - you are encouraged to set up a FaceBook group, with/without inviting the lecturers.
- Course FAQ
- The IVR course anthem - Bob Rocks! (by James Fairlie). And your favourite robot gives All Thanks to Turing.

This page is maintained by the course lecturer, Michael Herrmann,

`mherrman@inf.ed.ac.uk`

, room IF 1.42, ext 517177.

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