Registry

Module Specifications

Archived Version 2010 - 2011

Module Title	Computer Vision
Module Code	EE544
School	School of Electronic Engineering
Online Module Resources
Module Co-ordinator	Prof Paul F. Whelan	Office Number	S362
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The focus of this module is to produce graduates with a deeper theoretical understanding of the issues that underpin computer vision. It will build on the basic framework laid down in EE425/EE453 (or equivalent) with a view to delving deeper into to some of the topics introduced in previous modules. In addition it will introduce a range of advanced techniques and methodologies current in computer vision research. This module is primarily aimed at those who aim to undertake research in computer vision or require a deeper understanding of the subject to address commercial computer vision development. A significant element of the module will focus on developing independent learning skills for computer vision research. While we will not cover the prerequisite material in class, the course manual / textbook will contain the majority of this material for review by the student if required.

Learning Outcomes

1. Recall, review and analyse the advanced theories, algorithms, methodologies and techniques involved in computer vision.
2. Illustrate their ability to comprehend and interpret issues relating to the design of advanced computer vision.
3. Synthesize and evaluate the relevant merits of competing advanced computer vision techniques.
4. Apply computer vision techniques in a range of application scenarios.
5. Develop an deep understanding of the issues involved in the evaluating computer vision research.
6. Communicate complex technical issues to a wider audience.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
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 Completion	72	A significant element of this module is based on an independent learning focusing on a practical assignment in addition to a paper review and analysis assignment which will also evaluate the candistudents ability to communicate the ideas presented in a recent journal paper.
Directed learning	3	End of semester examination
Independent Study	30	Online activity with module material
Independent Study	46.5	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, Prerequisite Review (Examples)

Metrics

Automated Thresholding

Noise Reduction Techniques (1 & 2)

Image Classification (2)

Mathematical Morphology (2 & 3; Applications)

Colour Image Processing & Analysis (2)

Texture Analysis (2 & 3)

Eigenimage analysis

Motion & Tracking

Active Contours / Meshes / Models

Wavelets

3D Vision
Stereo vision / Depth from Defocus / Depth from focusing / Triangulation and laser scanning / Applications of 3D sensors to industrial processes

Recent computer vision research

Assessment Breakdown
Continuous Assessment	25%	Examination Weight	75%

Type	Description	% of total	Assessment Date
Course Work Breakdown

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
Unavailable

Indicative Reading List

Paul F Whelan: 0, Online course long form (including self assessment questions) and class notes (slides),
Milan Sonka, Vaclav Hlavac, Roger Boyle: 0, Image Processing: Analysis and Machine Vision,
Pierre Soille: 0, Morphological Image Analysis: Principles and Applications,
Richard Hartley and Andrew Zisserman: 0, Multiple View Geometry in Computer Vision,
Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins,: 0, Digital Image Processing Using MATLAB, 2nd ed., 0982085400
Paul F. Whelan, Derek Molloy,: 0, Machine Vision Algorithms in Java, 1-85233-218-2]
E. R. Davies: 0, Machine vision, 0122060938]
David A. Forsyth, Jean Ponce: 0, Computer vision, 0131911937
Richard O. Duda, Peter E. Hart, David G. Strok: 0, Pattern classification, 0471056693

Other Resources

734, Module Website, Paul F Whelan, 2010, EE544, http://elm.eeng.dcu.ie/~whelanp/ipa/protected_material/ipa_notes.html, 735, Module Software, Paul F Whelan, 2010, VSG Image Processing & Analysis Toolbox (VSG IPA TOOLBOX), http://www.cipa.dcu.ie/code.html, 736, Websites, 0, Online Image Processing References,

Programme or List of Programmes

BSSAO	Study Abroad (DCU Business School)
CAPD	PhD
CAPM	MSc
CAPT	PhD-track
ECSAO	Study Abroad (Engineering & Computing)
EEPD	PhD
EEPM	MEng
EEPT	PhD-track
GCES	Grad Cert. in Electronic Systems
GCTC	Grad Cert. in Telecommunications Eng.
GDE	Graduate Diploma in Electronic Systems
GTC	Grad Dip in Telecommunications Eng
HMSAO	Study Abroad (Humanities & Soc Science)
IPME	Individual Postgrad. Modules-Electronics
MEN	MEng in Electronic Systems
MEPD	PhD
MEPM	MEng
MEPT	PhD-track
MEQ	Masters Engineering Qualifier Course
MTC	MEng in Telecommunications Engineering
SHSAO	Study Abroad (Science & Health)

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