Advanced Vision Module

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10 Minute Introduction

The goal of this course is provide you with the skills to understand and sketch out solutions to a variety of computer vision applications. You should end up with the skills to tackle novel situations and incompletely defined applications. We will approach this by looking at 6 simplified computer vision systems that cover a large portion of the range of both applied and research computer vision.

Lectures:

Full class meeting times and rooms: Monday 2:10 pm (Venue: ***) and Thursday 2:10 pm (Venue: ***).
Lab demonstration sessions: Provisional Starting week 2, Monday 4-5, 5-6 (just come to 1 hour) in ***.

IMPORTANT INFORMATION: This course is not taught by the traditional lectures. Instead, AV uses an Inverted Classroom method. This means that you will have about 15 hours of video to watch in your own time. This material is assessible. There are still 2 full class meetings each week. Normally, at the first meeting we will be discussing any questions that you either suggest in advance or raise in class on the day. The second meeting will be a guest lecture teaching you some advanced vision technology and applications (these talks are not assessed).

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

Here is a proposed schedule of video watching and guest lectures:

WeekVideosMondayThursday
Jan 12 Introduction I:1-3, Flat parts II:1-2 Q&A Fisher: bats & fish?
Jan 19 Flat parts II:3-6 Q&A Beyan: unusual fish behaviour detection?
Jan 26 Detection and tracking VI:1-7 Q&A McDonagh: building models by combining multiple range images?
Feb 2 Detection and tracking VI:8-13, Deforming flat part recognition III:1-2 Q&A Fisher: skin cancer & blimp control?
Feb 9 Deforming flat part recognition III:3-8 Q&A Ferrari: object detection in colour images?
Feb 16 Innovative Learning Week
Feb 23 Range Image Analysis IV:1-8 Q&A ???
Mar 2 Persistent tracking and behavior recognition VII:1-6 Q&A ???
Mar 9 Stereo based 3D part recognition V:1-5 Q&A ???
Mar 16 Stereo based 3D part recognition V:6-11, Summary Q&A Horna: dense stereo?

Previous Lecture Recordings:

Summary of some previous recordings: 2013/4, 2012/3. Lecture audio & video recordings from 2009/10.

Lecturer

Bob Fisher, Informatics Forum 1.26, rbf @ inf.ed.ac.uk, (0131) 651-3441

Demonstrators

Demonstrators:

Before emailing with a question - please check the module FAQ

News

  Latest information, class announcements, handouts, etc

Syllabus

This module looks at several approaches to building vision systems. The order of the topics might change depending on the practicals.

In the process of doing this, we will encounter a variety of topics in low, middle and high level computer vision. There is also some review material from IVR on recognising orthographically viewed rigid flat objects.

Activities

  1. about 15 hours of lectures on the above syllabus are given, depending on the speed of delivery.
  2. Some guest lectures on recent research topics.
  3. 2 assessed laboratory practical exercises: 30 hours each. These laboratory exercises are done in teams of 2 to encourage development of team skills: teamwork exercises skills desired by employers and improves the learning process by encouraging discussion of topics.
  4. Outside reading and exam revision: 15 hours.
  5. Total: 100 hours

Feedback

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, and 5) 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.

Assessment

A 2 hour examination in the late spring accounts for 70% of the module mark and the practical work accounts for the other 30%. The practical work is split into two assessed exercises of 15% each.

The two practical exercises are:

  1. Intensity Image/Video Analysis (15%).
    Practical Handout [PDF here]. Announced by Jan 26.
    You must do this practical in teams of 2. Let the lecturer know who your partner is.
    Due: 4 pm Thursday 12 Feb. Marked results by 26 Feb.
    Submit PDF file on DICE using: submit av 1 PDF_FILE
    You will have to demonstrate your practical results on Friday 13 February from ***.
    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.

  2. 3D Image Analysis (15%).
    Practical Handout [PDF here]. Announced by March 2.
    You must do this practical in teams of 2 with a different partner from practical 1. Let the lecturer know who your partner is.
    Due: 4 pm Thursday 19 March. Marked results by 2 April.
    Submit PDF file on DICE using: submit av 2 PDF_FILE
    You will have to demonstrate your practical results on Friday 20 March from ***. 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.

Practicals 1 and 2 are done in groups of two. A single, joint, PDF report is to be submitted.

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.

Context

This module assumes the students have a secondary school understanding of geometry, matrix algebra, trigonometry, physics and programming concepts. Knowledge of elementary optics, signals and photography would be helpful. Students must be able to program and be able to work in small teams. Normally, a student will have attended Introduction to Vision and Robotics (IVR) before attending this module, but exceptions can be made if you have the necessary background. All of the assignments will be in Matlab. This was introduced in IVR, and some extensions will be introduced here. Please ensure familiarity with the concepts, techniques and practical aspects of the vision component of the IVR course.

There is a weekly Institute of Perception, Action and Behaviour seminar Thursday 11:00 (usually in IF 4.31) which covers related topics in vision and robotics. You should check the venue and dates at the IPAB seminars page

Other References, Course Notes and Other Study Materials

*** Lecture Slides
Note: that these are not written module notes. You are still expected to attend lectures and do the required reading.

*** Recommended course textbooks (All available electronically from the University library)
E.R. Davies - Machine Vision - Theory, Algorithms and Practice, Elsevier, 3rd Edition, 2005. (content for about 1/2 of course)

Solomon & Breckon, "Fundamentals of Digital Image Processing - A Practical Approach with Examples in Matlab", "Support Web Site", Wiley-Blackwell, 2010, ISBN: 978-0470844731. (content for about 1/2 of course)

R. Szeliski, "Computer Vision", Springer, 2011, ISBN: 978-1-84882-934-3. (content for about 1/2 of course)

** Optional supplementary textbooks
T. Morris - "Computer Vision and Image Processing" (Palgrave, 1st Edition, 2004)

D. A. Forsyth & J. Ponce - "Computer Vision - a modern approach" (Prentice Hall, 1st Edition, 2003)

** Online computer vision resources at University of Edinburgh (and beyond)

Various Online Resources


This page is maintained by the course lecturer, Bob Fisher, rbf@inf.ed.ac.uk, room IF 1.26, ext 513441.


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