Advanced Vision Module

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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. 10 Minute Introduction

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.


Bob Fisher, Informatics Forum 1.26, rbf @, (0131) 651-3441
Before emailing with a question - please check the module FAQ.


*** (s***)

Class Meetings:

Semester 2 full class meeting times and rooms: Monday and Thursday 2:10 pm (Venues: Monday: DHT Lower Ground 11, Thursday DHT Lower Ground 11).
Lab demonstration sessions: Starting week 2, Monday 4-5 or Thurs 4-5 (just come to 1 hour) in Appleton Tower level 3.

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. Each class will consist of 2 parts: 1) discussing any questions about the videos that you either suggest in advance or raise in class on the day. 2) There will be a simple non-assessed groupwork exercise to explore the issues raised in the videos that you have just watched.

Here is the link to the lecture videos, associated readings and associated Matlab in the University's LEARN system. You will need an EASE account to access this materials and then use the 'Login' button at the upper right of the screen. Then click on the large Login with EASE button. After that click on "Advanced Vision (Level 11) (2017-2018)[SV1-SEM2]". You should: (a) Read the introduction "Summary of teaching materials and approach". (b) Watch the materials for the corresponding week (as given in the table below) by the Monday class. (c) Get access to the materials by clicking, for example, on 'Course introduction and review' (left edge) -> '2. Coordinate geometry transformation review' (right panel). This exposes the materials. (d) Read the lesson plan, download the PDF slides to be annotated while you watch the video, and then try to answer the Review Question.

Here is an Introduction to how the course is intended to be followed.

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

Jan 15 Introduction: modules 1-3, Flat parts System 1: modules 1-2 Course Intro Q&A + drill
Jan 22 Flat parts System 1: modules 3-6 Q&A + drill Q&A + drill
Jan 29 Detection and tracking System 3: modules 1-7 Q&A + drill Q&A + drill
Feb 5 Detection and tracking System 3: modules 8-12, Deforming flat part recognition System 2: modules 1-2 Q&A + drill Q&A + drill
Feb 12 Deforming flat part recognition System 2: modules 3-8 Q&A + drill Q&A + drill
Feb 19 Break Week
Feb 26 Range Image Analysis System 4: modules 1-8 Q&A + drill Q&A + drill
Mar 5 Persistent tracking and behavior recognition System 5: modules 1-6 Q&A + drill Q&A + drill
Mar 12 Stereo based 3D part recognition System 6: modules 1-5 Q&A + drill Q&A + drill
Mar 19 Stereo based 3D part recognition System 6: modules 6-11 + Course Summary Q&A + drill Previous Exam Review


  Latest information, class announcements, handouts, etc


  1. about 15 hours of lectures on the above syllabus are given, depending on the speed of delivery.
  2. Some supplementary readings that explain the lecture content in a different way.
  3. 2 assessed laboratory practical exercises: 20 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


A 2 hour examination in the late spring accounts for 75% of the module mark and the practical work accounts for the other 25%. You can see past exam papers here.

The practical exercise is:

  1. 3D Image Analysis (15%).
    Practical Handout [PDF here]. Announced by ***.
    You must do this practical in teams of 2. Let the lecturer know who your partner is.
    Due: 4 pm Thursday 22 March. Marked results by 5 April.
    Submit PDF file on DICE using: submit av 1 PDF_FILE
    You will have to demonstrate your practical results on Friday 23 March from 9:00-13:00.
    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.

The practical is done in groups of two. A single, joint, PDF report is to be submitted. For distance learning students: you still have to work in teams of two. If you're not in the same city, then communicate by skype and jointly work on the practical. Decide how to split the work. You should be able to use matlab on the university computers, to which you will have an account. However, it may be difficult to display images remotely, so it is probably better and easier for you to buy a Matlab student license.

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.


Other Information

This page is maintained by the course lecturer, Bob Fisher,, room IF 1.26, ext 513441.

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