Computer Science

ECMM426 - Computer Vision (2019)

Back | Download as PDF
MODULE TITLEComputer Vision CREDIT VALUE15
MODULE CODEECMM426 MODULE CONVENERDr Anjan Dutta (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 15 0
Number of Students Taking Module (anticipated) 5
DESCRIPTION - summary of the module content

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, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in computer vision have seen significant improvement in important applications (face detection in cameras, body tracking, and autonomous cars).

This module expects prior knowledge in linear algebra and probability theory (e.g., ECM1416), object oriented programming (ECM1414 and ECM1410). Prior knowledge in machine learning is also desirable (such as ECM3420 or ECMM422).

AIMS - intentions of the module

This module will provide you with the fundamentals of computer vision, covering the essential challenges and key algorithms for solving a variety of vision problems. The course will provide both theoretical grounding in the relevant theories and a blend of classical and state-of-the-art approaches to computer vision problems. The course will focus on practical applications of computer vision and cover a broad range of problems, from low-level image processing to object recognition, tracking and 3D vision.

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 problems and their mathematical formulation.
2. Design and implement vision algorithms in a high-level language.

Discipline Specific Skills and Knowledge

3. Analyse and propose solutions for computer vision 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 the state-of-the-art.
6. Critically read and report on research papers.

 

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

The course will cover the following topics:

  • Image formation: geometry, light and cameras
  • Image processing: convolution, linear filters, Fourier transforms, image gradients, geometric transformations
  • Feature extraction & matching: corners, edges, blobs and lines; feature descriptors (SIFT), feature matching and tracking
  • Object detection and recognition: K-NN, bag-of-words, scanning windows & Viola-Jones
  • Image segmentation: active contours, Markov random fields, graph cuts
  • Dense image correspondences: dense motion estimation, optical flow, stereo
  • Shape reconstruction: 2D and 3D shape modelling and fitting, active appearance models, 3D morphable models
  • 3D vision: 3D pose estimation, calibration, structure from motion, SLAM, shape from shading, motion capture
  • Deep learning for vision: neural networks, convolutional neural networks, recurrent neural networks
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33.00 Guided Independent Study 117.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching activities 22 Lectures
Scheduled Learning & Teaching activities 11 Workshops/tutorials
Guided independent study 48 Project and coursework preparation
Guided independent study 69 Wider reading

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
SUMMATIVE ASSESSMENT (% of credit)
Coursework 60 Written Exams 40 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework: workshop code 1 10 8 hours, code submission All Oral and model answers
Coursework: workshop code 2 10 8 hours, code submission All Oral and model answers
Coursework: workshop code 3 10 8 hours, code submission All Oral and model answers
Coursework: workshop code 4 10 8 hours, code submission All Oral and model answers
Project 20
16 hours, submission of
3-pages report & code
All In writing
Examination 40 2 hours 1,3,4,5,6 In writing
         

 

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
Summative Assessment Examination (100%) 1,3,4,5,6 August assessment period
       
       

 

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

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Szeliski, Richard. Computer vision: algorithms and applications Springer 2011 [Library]
Set David Forsyth & Jean Ponce Computer vision: a modern approach 2nd Pearson 2011 [Library]
Set John C. Bishop Pattern recognition and machine learning Springer 2006 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Friday 09 August 2019
KEY WORDS SEARCH Computer vision, object recognition, tracking, pattern recognition, deep learning.