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Study information

Machine Vision - 2019 entry

MODULE TITLEMachine Vision CREDIT VALUE15
MODULE CODEECMM441 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***

How do we recognise objects and people?  How can we catch a ball?  How do we navigate our way from our desk to the coffee machine, withouth bumping into each other?  These seemingly simple tasks have represented a challenge for AI scientists for decades.  Recent developments in machine vision have seen significant improvement in important applications (face detection cameras, body tracking, and autonomous cars).

Pre-requisites: ECMM430 Fundamentals of Data Science, ECMM434 Machine Learning & Statistical Modelling.
Co-requisites: None.

AIMS - intentions of the module

This module will provide you with the fundamentals of machine vision and image processing, covering the essential challenges and key algorithms for solving a variety of problems related to automated processing of visual data. The course will provide both theoretical grounding and a practical introduction to classical and state-of-the-art approaches. Theory and methods will be taught through practical applications of machine vision and will cover a broad range of problems, from low-level image processing to object recognition, motion tracking and 3D vision.

Content will be delivered in an intensive one-week teaching block consisting of lectures and practical work. Self-study and coursework will complete the module teaching activities.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Explain key computer vision and image processing problems and their mathematical formulation.
2. Design and implement vision and image processing algorithms in a high-level language.

Discipline Specific Skills and Knowledge

3. Analyse and propose solutions for computer vision and image processing problems.
4. Select appropriate statistical representations, features and algorithms to suit problem specificities.

Personal and Key Transferable / Employment Skills and Knowledge

5. Understand and appreciate the limitations of different methods.
6. Effectively communicate to a technical audience using reports and documentation.
7. Critically read and report on research papers.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics will include:

  • Image processing: convolution, linear filters, Fourier transforms, image gradients
  • Feature extraction & matching: edge & corner detection, multi-scale analysis, feature descriptors, feature matching and tracking
  • Geometric Image formation: geometric transformations, pinhole camera and perspective effects
  • Multi-view geometry and structure from motion
  • Dense image correspondences: dense motion estimation, optical flow, stereo
  • Recognition in Computer Vision: Classical approaches, Neural Networks and Convolutional Neural Networks
  • Shape reconstruction: 2D and 3D shape modelling and fitting, active appearance models, 3D morphable models, motion capture

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 32 Guided Independent Study 118 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 16 Lectures
Scheduled Learning and Teaching 16 Practicals
Guided independent study 50 Coursework preparation
Guided independent study 68 Background reading and self study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Practical work 16 hours All Oral
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 1 30 submission of 3-pages report & code All Written
Coursework 2 30 submission of 3-pages report & code All Written
Online quiz 1 20 1 hour 1,3,4,5 Written
Online quiz 2 20 1 hour 1,3,4,5 Written
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Coursework Coursework All Within 8 weeks
Online quiz Online quiz 1,3,4,5 Within 8 weeks
       

 

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

CVOnline: an online compendium of computer vision techniques: http://homepages.inf.ed.ac.uk/rbf/CVonline/

Website of R. Szelinski’s Computer Vision book (including a free electronic version of the book): http://szeliski.org/Book

Getting Started with MATLAB: http://uk.mathworks.com/help/matlab/getting-started-with-matlab.html

Computer Vision System Toolbox of MATLAB: https://uk.mathworks.com/products/computer-vision.html

Keras: The Python Deep Learning library: https://keras.io/

Other Resources:

Szeliski, Richard.

Computer vision: algorithms and applications

 

Springer

2011

Ian Goodfellow, Yoshua Bengio & Aaron Courville  

Deep Learning   

 

MIT Press

2016

David Forsyth & Jean Ponce

Computer vision: a modern approach

2nd

Pearson

2011

John C. Bishop

Pattern recognition and machine learning

 

Springer

2006

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Jan Erik Solem Programming Computer Vision with Python: Tools and algorithms for analyzing images O'Reiley 2012
Set Szeliski, Richard. Computer vision: algorithms and applications Springer 2011
Set Forsyth, David & Jean Ponce Computer vision: a modern approach 2nd Pearson 2011
Set Bishop, John C. Pattern recognition and machine learning Springer 2006
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECMM430, ECMM434
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 6th July 2017 LAST REVISION DATE Wednesday 19th December 2018
KEY WORDS SEARCH Computer vision, image processing, object recognition, tracking, pattern recognition.

Please note that all modules are subject to change, please get in touch if you have any questions about this module.