Registry

Module Specifications

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

Module Title	Image Processing & Analysis with Project
Module Code	EE453
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	7.5
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 both the theoretical, mathematical and 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) and computer vision (image to interpretation) 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 covers during the lectures.In addition to the common elements associated with EE425, this module will contain a significant competitive group image processing & analysis project. Students will have the opportunity to develop a commercial level solution in one of two areas: biomedical or industrial computer vision. The focus of the project will be on developing engineering solutions rather than a research projects. Students will be expected to submit individual independent reports (of a fixed length) to be presented in the form of a technical paper communication. In addition to the engineering issues, the report must outline both the commercial potential and the ethical issues involved with the work.

Learning Outcomes

1. Recall, review and analyse the essential theories, algorithms, methodologies and techniques involved in computer vision.
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 computer vision techniques.
4. Apply computer vision techniques in a range of application scenarios.
5. Develop an understanding of the engineering issues involved in the commercial development of image processing and analysis solutions.
6. Illustrate the ability to place a vision engineering project in the context of both commercial and ethical realities.
7. Communicate technical and non technical issues in a group based environment.

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	36	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, computer vision 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].
Assignment	30	Pratical assignments
Directed learning	62.5	Project
Independent learning	3	End of Semester Exam
Independent learning time	20	Online activity with module material
Independent learning	36	General revision and practice
Total Workload: 187.5

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.

Performance Analysis.
Systems Engineering.

Case Studies.
Ethics.

Sample Problems & Review.
Project.

Assessment Breakdown
Continuous Assessment	50%	Examination Weight	50%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Project	In addition to the common elements associated with EE425, this module will contain a significant competitive group image processing & analysis project.	25%	n/a
Practical/skills evaluation	Practical Assignment	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 3

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, 0122060938]
Paul F Whelan: 2010, Online Course long form 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

1179, Module Website, Paul F Whelan, 2010, EE453 & EE425, http://elm.eeng.dcu.ie/~whelanp/ipa/protected_material/ipa_notes.html, 1180, Module Software, Paul F Whelan, 2010, VSG Image Processing & Analysis Toolbox (VSG IPA TOOLBOX), http://www.cipa.dcu.ie/code.html, 1181, Websites:, 0, Online Image Processing References,

Array

Programme or List of Programmes

BMED

B.Eng. in Biomedical Engineering

DME

B.Eng. in Digital Media Engineering

ECSAO

Study Abroad (Engineering & Computing)

BEng in Electronic Engineering

EEV

M.Eng. in Electronic Engineering

MEN