Engineering

ECMM104 - Signal Analysis and Image Processing (2015)

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MODULE TITLESignal Analysis and Image Processing CREDIT VALUE15
MODULE CODEECMM104 MODULE CONVENERProf Mike Belmont (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 weeks 0
Number of Students Taking Module (anticipated) 4
DESCRIPTION - summary of the module content

This module builds upon signal processing and system theory modules to deliver a professional-level course in modern signal processing aimed at causal time series and non-causal image data. The module starts by considering the key real world aspects of all signals, i.e. sampling and truncation effects then examines an increasingly sophisticated range of operations.  These operations start with low level sample/pixel processes, move through global statistical methods to filtering and stochastic identification techniques.  The assessment is entirely based on a large software assignment with assessment employing a teaching contract methodology.

 

 

Prerequisite module: ECM2105, ECM2111, ECM2117 or equivalent
 


 

 

AIMS - intentions of the module

This module will provide you with directly applicable professional engineering level skills in the areas of time series processing and spatial image processing.

 

This module covers Specific Learning Outcomes in Engineering, which apply to accredited programmes at Bachelors/MEng/Masters level. These contribute to the  educational requirements for CEng registration (as defined under the UK Standard for Professional Engineering Competence – UK-SPEC).



This module correlates to references E3, MU3 and ME1 - ME3. These references are indices of the specific learning outcomes expected of Bachelors/MEng/Masters candidates set out in UK-SPEC, codified with reference to systems used by professional accrediting institutions. A full list of the standards can be found on the Engineering Council's website, at http://www.engc.org.uk

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 demonstrate understanding of the detailed quantitative effects of sampling  and windowing of continuous functions by arbitrary sample comb and window functions;
2 design discrete algorithms to minimise the effects of sampling and windowing in a controlled manner;
3 apply orthogonal and non-orthogonal function sets to represent data, including FAST algorithm realisations;
4 understand the distinctions between local and globally stationary data and be able to select and apply appropriate function steps to represent such data. Examples covered include Fourier, Gabor and Wavelet transforms;
5 understand and be able to apply various de-noising and data compression techniques;
6 implement causal and 1D causal and 2 D non-causal linear filters, including FIR and IIR and using frequency domain methods;
7 apply low level pixel based operations on images, including modifying image statistics using techniques such as Histogram Renormalisation;
8 apply adaptive line detection techniques;
9 apply correlation mask techniques for object identification and parameterisation;
10 demonstrate understanding of Self-Similar systems;
11 apply various of the above techniques to achieve Image Segmentation;
12 demonstrate understanding of introduction to Compression and Signal Synthesis techniques.
Discipline Specific Skills and Knowledge:
13 demonstrate an understanding of the use of signal and image analysis and processing in modern electronic and software systems.
Personal and Key Transferable/ Employment Skills and  Knowledge:
14 assess their own competence during the negotiation of a learning contract and its implementation;
15 demonstrate awareness of the difference between a professional piece of work and a purely academic exercise;
16 obtain and apply information independently;
17 communicate professionally through oral discussion and written presentation.

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

- detailed quantitative effects of sampling and windowing of continuous functions by arbitrary sample comb and window functions;

- design of discrete algorithms to minimise the effects of sampling and windowing in a controlled manner;

- application of orthogonal and non-orthogonal function sets to represent data, including FAST algorithm realisations, local and globally stationary data, selection and application of appropriate function sets to represent such data;

- examples covered include Fourier, Gabor and Wavelet transforms;

- de-noising and data compression techniques;

- implementation of 1D causal and 2 D non-causal linear filters, including FIR and IIR and using frequency domain methods;

- application of low level pixel based operations on images, including modifying image statistics using techniques such as Histogram Renormalisation;

- adaptive line detection techniques;

- correlation mask techniques for object identification and parameterisation;

- self-similar systems;

- application of above techniques to achieve image segmentation;

- compression;

- signal synthesis.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 22.00 Guided Independent Study 128.00 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 22 Directed study tutorials
Guided independent study 128 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
Checking weekly software assignments that provide practice towards the module assessment contract Write one off signal processing function Fundamental understanding and software skills Individual verbal feedback
       
       
       
       

 

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 – development of a major image processing software package. 100 Approximately two weeks worth of full time work All WrittenIndividual debriefing with student while observing the operation of their software package
         
         
         
         

 

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 50% 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 Gonzalez R C and Woods R E Digital Image Processing 3rd Addison, Wesley 2007 978-0135052679 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM2117, ECM2105, ECM2111
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) M (NQF level 7) AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 09 January 2015 LAST REVISION DATE Wednesday 25 November 2015
KEY WORDS SEARCH None Defined