Computer Science

ECMM403 - Intelligent Image Understanding (2015)

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MODULE TITLEIntelligent Image Understanding CREDIT VALUE15
MODULE CODEECMM403 MODULE CONVENERDr Jovisa Zunic (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 weeks 0
Number of Students Taking Module (anticipated) 3
DESCRIPTION - summary of the module content

In recent decades, image technologies have advanced rapidly, and many images and much image- based data have become available in different domains, from biology to astrophysics,  industry to medicine. Such images carry useful information and this module presents some of the techniques used for analysing objects presented on images and how they are matched, recognised, identified and classified.

AIMS - intentions of the module

The main aim of this module is to introduce you to computer understanding of digital images of real objects. You will learn about various image processing methods that allow computers to select and recognise particular objects, segment them into regions of interest, extract suitable features, and enable an efficient classification of the considered objects.

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 write dedicated software for the computer analysis of digital images taken from a variety of applications;
2 apply standard methods of  feature extraction, invariants,  and shape descriptors on digital images;
3 develop their independent approaches to developing algorithms on image understanding.
Discipline Specific Skills and Knowledge:
4 demonstrate a good theoretical and applied knowledge on intelligence in image processing in a range of areas.
Personal and Key Transferable/ Employment Skills and  Knowledge:
5 select and use appropriate tools for problems solving;
6 communicate effectively both in written and oral presentations.
SYLLABUS PLAN - summary of the structure and academic content of the module

The syllabus for the module can be structured as below:

 

-  image processing methods: definition of images, resolution, image features, moments, moments invariants;
- shape analysis methods: boundary-based shape descriptors, area based shape descriptors, computation of shape descriptors,statistical methods for describing shape, encasing objects, human perception consideration;
- curves, regression;
- object encoding: chain codes, filters, histograms;
- classification algorithms.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 28.00 Guided Independent Study 122.00 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 16 Lectures
Scheduled learning and teaching activities 12 Workshops
Guided independent study 122 Guided independent 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
Not applicable      
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework – coursework 25 A number of theoretical questions/exercises 2,4,5 Written
Coursework – project 75 4,000 words 1,2,3,6 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-reassessment
All above Coursework (100%) All Completed over summer with a deadline in August
       
       

 

RE-ASSESSMENT NOTES

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.


If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

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

ELE – http://vle.exeter.ac.uk

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Sonka M, Boyle R, Hlavac V Image processing, Analysis, and Machine Vision Brooks/Cole 2007 0-534-953-93-X [Library]
Set Klette and Rosenfeld Digital Geometry - Geometric Methods for Digital Picture Analysis Morgan Kaufmann 2004 1-55860-861-3 [Library]
Set Duda and Hart Pattern Classification and Scene Analysis 2nd Wiley 2002 0471056693 [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 Friday 09 January 2015 LAST REVISION DATE Friday 09 January 2015
KEY WORDS SEARCH Image technologies; software; digital images; image-based data.