CS3IA16-Image Analysis

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Autumn / Summer term module
Pre-requisites: CS1MA16 Mathematics for Computer Science or MA1LA Linear Algebra
Non-modular pre-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Dr Hong Wei

Email: h.wei@reading.ac.uk

Type of module:

Summary module description:

 The module delivers a set of topics involved in image analysis, such as image enhancement, image compression, image segmentation, and colour image processing. Relevant techniques are introduced in lectures and practised in assigned lab-based coursework. 


The module aims to provide students with theoretical and practical knowledge of digital image processing and analysis from various techniques and applications.

This module also encourages students to develop a set of professional skills, such as problem solving, critical analysis of published literature, creativity, technical report writing for technical and non-technical audiences, self-reflection and effective use of commercial software.

Assessable learning outcomes:

Students who complete this module will have:

  • basic skills for image analysis;

  • the ability to address issues associated with image transformation, histogram analysis and modification, image morphological operations and colour image manipulation;

  • skills to develop algorithms for digital image enhancement, image compression and texture-based image segmentation.

Additional outcomes:

Programming skills can be improved from coursework assignments.

Outline content:

This module covers the following topics:

  • digital image fundamentals;

  • image enhancement in the spatial domain;

  • image enhancement in the frequency domain;

  • colour image processing;

  • mathematical morphology in image processing;

  • image compression;

  • image segmentation.

Brief description of teaching and learning methods:
Lectures supported by laboratory practicals and tutorials.

Contact hours:
  Autumn Spring Summer
Lectures 16 1
Tutorials 2
Guided independent study: 81
Total hours by term 0
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Written exam 70
Set exercise 30

Summative assessment- Examinations:

One 2-hour examination paper in May/June.

Summative assessment- Coursework and in-class tests:

Two pieces of coursework, and each takes 15% of the module assessment.

Formative assessment methods:

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
A mark of 40% overall

Reassessment arrangements:

One 2-hour examination paper in August/September.  Note that the resit module mark will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (70% exam, 30% coursework).  

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 16 April 2020


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