Registry

Module Specifications

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

Module Title	Computer Vision
Module Code	EE544
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

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 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.

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	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.
Examination	3	End of semester examination
Independent learning time	30	Online activity with module material
Independent learning time	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.
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	40%	Examination Weight	60%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Practical/skills evaluation	Practical Assignment	20%	n/a
Practical/skills evaluation	Research Assignment	20%	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: 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

1360, Module Website, Paul F Whelan, 2010, EE544, http://elm.eeng.dcu.ie/~whelanp/ipa/protected_material/ipa_notes.html, 1361, Module Software, Paul F Whelan, 2010, VSG Image Processing & Analysis Toolbox (VSG IPA TOOLBOX), http://www.cipa.dcu.ie/code.html, 1362, Websites, 0, Online Image Processing References,

Array

Programme or List of Programmes

BMEDV

M.Eng. in Biomedical Engineering

CAPD

PhD

CAPM

MSc

CAPT

PhD-track

DMEV

M.Eng. in Digital Media Engineering