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. See the AV Learn page for links to Matlab tutorials.


Bob Fisher, Bayes 1.11, rbf @, (0131) 651-3441
Before emailing with a question - please check the module FAQ.


Hanz Cuevas Velasquez (s1678460)

Class Meetings:

Semester 2 full class meeting times and rooms: Monday and Thursday 2:10 pm (Venues: Monday: Lister Institute G.01, Thursday DHT Lower Ground room 9).

Lab demonstration sessions: Starting week 2, Monday 4-5, Tuesday 4-5, Thursday 4-5, or Friday 4-5 (just come to 1 hour) in Appleton Tower room 5.08 South Lab.
There are 5 non-assessed lab exercises that will help you develop your matlab and image handling skills:

  1. File handling: lab_1.pdf, lab_1.tar.gz
  2. Image processing: AVTutorial_2.pdf, tutorial_2.tar.gz
  3. Change detection: AVTutorial_3.pdf, tutorial_3.tar.gz
  4. 3D point data: lab_4.pdf,
  5. ***: lab_5.pdf,
See also the AV Learn page for links to Matlab tutorials.

Lecture Plan

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) (2018-2019)[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 14 Introduction: modules 1-3; Visual Ethics; Flat parts System 1: modules 1-3 Course Intro Q&A + drill
Jan 21 Flat parts System 1: modules 4-6 Detection and tracking System 3: modules 1-4 Q&A + drill Q&A + drill
Jan 28 Detection and tracking System 3: modules 5-12 Q&A + drill Q&A + drill
Feb 4 Range Image Analysis System 4: modules 1-8 Q&A + drill Q&A + drill
Feb 11 Stereo based 3D part recognition System 6: modules 1-5 Q&A + drill Q&A + drill
Feb 18 Festival of Creative Learning Week no class no class
Feb 25 Stereo based 3D part recognition System 6: modules 6-11 Q&A + drill Q&A + drill
Mar 4 Deforming flat part recognition System 2: modules 1-8 Q&A + drill Q&A + drill
Mar 11 Persistent tracking and behavior recognition System 5: modules 1-6 Q&A + drill Q&A + drill
Mar 18 Deep Nets for Vision Modules 1-4 + 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. 1 assessed laboratory practical exercise: 30 hours for 30% of course mark. 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 70% of the module mark and the practical work accounts for the other 30%. You can see past exam papers. Go to:, enter "Advanced Vision" and select "Advanced Vision (Level 11)". Click "Search".

The practical exercise is:

  1. 3D Image Analysis (30%).
    Practical Handout [PDF here]. Announced by Feb 1.
    You must do this practical in teams of 2. Let the lecturer know who your partner is.
    Most students do the assignment in Matlab, and some with Python. You can use whatever language you want, but we do not provide programming support.
    This is a 4 stage assignment (see assignment handout for more details):
    (1) Initial full assignment submission (0%): Tuesday March 19, 4pm. Submit PDF file on DICE using: submit av 1 PDF_FILE
    (2) Feedback given to another team on their assignment (5%): Thursday March 21, 4pm. Submit PDF file on DICE using: submit av 2 PDF_FILE
    (3) Final report submission (20%): Thursday March 28, 4pm. Submit PDF file on DICE using: submit av 3 PDF_FILE
    (4) Live demonstration of practical (5%): Friday March 29, 9:00-13:00 in room AT 5.04. Each team will have a scheduled time.
    Marked results by 12 April.
    Feedback: You will get (1) written feedback from the team reviewing your submission, (2) oral feedback at the demonstration, and (3) 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 online 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.

Discussion Forum

The AV course has a Piazza Discussion Forum. Please use this for question asking and discussion as you will probably get a faster answer from your colleagues and the TA than by direct email to Bob Fisher. Most students have been enrolled for the AV Piazza Forum. If you have not been enrolled, email


Other Information

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

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