Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Image Processing and Analysis
Module Code	EE425
School	School of Electronic Engineering
Online Module Resources
Module Co-ordinator	Semester 1: Paul Whelan Semester 2: Paul Whelan Autumn: Paul Whelan
Module Teacher	Paul Whelan
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

Most people are familiar with the concept of processing an image to improve its quality or the use of image analysis software tools to make basic measurements; but what are the ideas behind such solutions and why is knowledge of these concepts important in developing successful computer vision applications? This module will answer these questions by focusing on the practical issues associated with a wide range of computer vision solutions. Such solutions relate to the fields of image processing & analysis, industrial/machine vision, video data processing, biomedical engineering, imaging science, sensor technology, multimedia and enhanced reality systems.This module will concentrate on developing the fundamentals necessary to design, develop and understand a wide range of basic imaging processing (image to image), image analysis (image to feature), image classification (feature to decision), performance characterisation (data to quantitative performance indicators) solutions. All solutions have limitations and a key element of this module is to focus on how to approach the design, testing and evaluation of successful computer vision applications within an engineering framework. This module will make extensive use of an image analysis development environment to reinforce all the issues covered during the lectures.

Learning Outcomes

1. Recall, review and analyse the essential theories, algorithms, methodologies and techniques involved in image processing & analysis
2. Illustrate their ability to comprehend and interpret issues relating to the design of image processing & analysis techniques.
3. Synthesize and evaluate the relevant merits of competing image processing & analysis techniques.
4. Apply computer vision techniques in a range of application scenarios.

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	This module is presented in a traditional format (lecture and continuous assessment) with significant practical support [including: long-format electronic notes and associated course text, pdf versions of the class slides, mailing list support, image analysis development environment (used for the assignments and to illustrate computer vision concepts), self assessment questions and selected examples illustrating key concepts are also presented along with their associated images/data].
Tutorial	12	Support
Assignment	30	Practical Assignments
Examination	2	End-of-Semester Final Examination
Independent learning time	15	Online activity with module material
Independent learning time	42	General revision and practice
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction, Tutorial on course tools.
Image Representation.

File Formats, Point Operations, Neighbourhood Operations, Distance transform.
Feature Extraction, Image Analysis, Local Operators.

Image Classification (1).
Global Image transforms.

Geometric, Hough, DFT, DCT.
Mathematical Morphology (1).

Binary and Grey Scale.
Colour Image Processing & Analysis (1).

Texture Analysis (1).
Interest Point Detection.

Image Acquisition.
Optics, Lighting and Sensors.

Systems Engineering.
Performance Analysis.

Case Studies.
Ethical Issues.

Sample Problems & Review.

Assessment Breakdown
Continuous Assessment	25%	Examination Weight	75%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Practical/skills evaluation	Practical Assignments	25%	n/a

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Paul F. Whelan, Derek Molloy,: 2000, Machine Vision Algorithms in Java, Springer, 1-85233-218-2
Milan Sonka, Vaclav Hlavac, and Roger Boyle: 2008, Image processing, analysis, and machine vision, Thompson Learning, Toronto, 978-0495082521
E. R. Davies: 0, Machine vision, Morgan Kaufmann, 0122060938
Paul F Whelan: 2010, Online Course long form notes (including self assessment questions) and class notes (slides),
Rafael C. Gonzalez, Paul Wintz: 1987, Digital image processing, Addison-Wesley, Reading, Mass., 0201110261
Rafael C. Gonzalez, Richard E. Woods: 1992, Digital image processing, Addison-Wesley, Reading, Mass., 0201508036
Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins,: 0, Digital Image Processing Using MATLAB, 2nd ed., 0982085400

Other Resources

18, Module Website, Paul F Whelan, 2010, EE425 & EE453, http://elm.eeng.dcu.ie/~whelanp/ipa/protected_material/ipa_notes.html, 19, Module Software, Paul F Whelan, 2010, NVSG Image Processing & Analysis Toolbox (VSG IPA TOOLBOX), http://www.cipa.dcu.ie/code.html, 20, Websites, 0, Online Image Processing References,

Array

Programme or List of Programmes

BSSA

Study Abroad (DCU Business School)

BSSAO

Study Abroad (DCU Business School)

ECSA

Study Abroad (Engineering & Computing)

ECSAO

Study Abroad (Engineering & Computing)

HMSA

Study Abroad (Humanities & Soc Science)