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
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.
Hanz Cuevas Velasquez (s1678460)
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:
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 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|
The practical exercise is:
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.
All practicals are covered by the school policy on plagiarism and students are advised to be fully aware of this when submitting practical work.
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 email@example.com.
firstname.lastname@example.org, room IF 1.26, ext 513441.
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