Registry

Module Specifications

Archived Version 2009 - 2010

Module Title	Computer Vision
Module Code	EE453
School	School of Electronic Engineering
Online Module Resources
Module Co-ordinator	Prof Paul F. Whelan	Office Number	S362
Level	4	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None

Module Aims

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.

Learning Outcomes

On completion of this module, the student will be able to
Recall, review and analyse the essential theories, algorithms, methodologies and techniques involved in computer vision. (PO1, PO2)
Illustrate their ability to comprehend and interpret issues relating to the design of image processing & analysis techniques. (PO1, PO2, PO3, PO5, PO6)
Synthesize and evaluate the relevant merits of competing computer vision techniques. (PO1, PO5)
Apply computer vision techniques in a range of application scenarios. (PO1, PO2, PO3, PO5, PO6)

Indicative Time Allowances
	Hours
Lectures	36
Tutorials
Laboratories
Seminars
Independent Learning Time	76.5

Total	112.5
Placements
Assignments

NOTE

Assume that a 7.5 credit module load represents approximately 112.5 hours' work, which includes all teaching, in-course assignments, laboratory work or other specialised training and an estimated private learning time associated with the module.

Indicative Syllabus

• Introduction; Tutorial on course tools • Image Representation, Point Operations; Neighbourhood Operations • Signal processing; Noise reduction• Feature Extraction; Image Analysis; Local Operators • Image Classification • Global Image transforms: Geometric, Distance, Hough, DFT, DCT • Mathematical Morphology: Binary and Grey Scale • Colour Image Processing & Analysis• Corners / Edges; Active contours• Texture Analysis • Motion analysis / Tracking • Performance analysis • Sample Problems & Review

Assessment
Continuous Assessment	25%	Examination Weight	75%

Indicative Reading List

Online Course long form notes and class notes (slides)Machine Vision Algorithms in Java: Techniques and Implementation, P.F. Whelan and D. Molloy, Springer (2000), ISBN 1-85233-218-2 Image Processing, Analysis and Machine Vision, M. Sonka, V. Hlavac and R. Boyle, Chapman & Hall (1993).

Contribution to Programme Areas:

Science & Mathematics	Discipline - specific Technology	Information and Communications	TechnologyDesign and Development	Engineering PracticeSocial and Business Context	Social and Business Context
3	4	3	4	4	1

Contribution to Programme Outcomes:

Knowledge and its application	Problem Solving	Design	Ethical Practice	Effective Work and Learning	Effective Communication
4	4	4	1	3	3

Teaching & Learning Strategies/Assessment Methodology:
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].

This module will require basic programming skills. The course makes use of a suitable computer vision development environment to keep the students focus on the issues relating to computer vision solution design rather than programming. The module will also require basic undergraduate engineering mathematics (e.g. matrices, vectors, differential equations, Fourier, trigonometry, algebra ...) with a particular focus on discrete systems. Selected areas will be revisited throughout the module.

Additional online resources are employed where appropriate. A significant element of this module is based on a problem based learning methodology using class examples and case studies in conjunction with two project assignments as part of the modules continuous assessment.

Programme or List of Programmes

BSSA

Study Abroad (DCU Business School)

BSSAO

Study Abroad (DCU Business School)

DME

B.Eng. in Digital Media Engineering

ECSA

Study Abroad (Engineering & Computing)

ECSAO

Study Abroad (Engineering & Computing)

BEng in Electronic Engineering