Module: Image Processing & Analysis

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Image Processing & Analysis
Module Code	EEN1039 (ITS: EE425)
Faculty	Electronic Engineering	School	Engineering & Computing
NFQ level	8	Credit Rating	5

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 a standard 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 image processing and analysis techniques to a range of application scenarios.
5. Demonstrate the ability to implement a image processing and analysis pipeline.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	This module is presented in a traditional format (lecture and continuous assessment) with significant practical support.
Laboratory	12	Laboratory support for coursework & tutorials
Independent Study	89	General revision and practice, Coursework, Online activity with module material. Homeworks and tutorials.
Total Workload: 125

Section Breakdown
CRN	11028	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Julia Dietlmeier	Module Teacher	Jaime Boanerjes Fernandez Roblero, Paul Whelan

Type	Description	% of total	Assessment Date
Assessment Breakdown
Practical/skills evaluation	Practical Assignments	25%	n/a
Formal Examination	End-of-Semester Final Examination	75%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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
• Introduction to the software development environment for Image Processing & Analysis • IPA Pipeline • Learning Outcomes • Module Protocol • Assessment Requirement • Code Development • Support Material & Website • Human/Computer/Machine Vision • Ethics • Case Studies.

Basic Techniques
• Image Representation • Point Operators • Thresholding • Local Operators • Non-linear Local Operators • Template Matching • Histograms • Binary Images • Simple Shape Descriptors • Edge Detection • Corner Detector

Morphology
• Binary Mathematical Morphology • Grey Scale Morphology • Top-Hat Transform • Covariance • Conditional Dilation • Reconstruction by Dilation • Opening/Closing by Reconstruction

Transforms
• Global Image Transforms • Distance Transform • Hough Transform • Two-dimensional Discrete Fourier Transform

Classification & Performance Characterisation
• Supervised vs Unsupervised • Feature Selection • Nearest Neighbour Classifier (KNN) • Maximum-likelihood Classifier • Performance Characterisation

Colour
• Human Perception of Colour • Colour Spaces • Colour Scattergrams • Programmable Colour Filter

Texture
• Histogram Features • Co-occurrence (Matrix) Approach • Morphological Texture Analysis • Local Binary Patterns (LBP)

Indicative Reading List

Books:

Paul F Whelan: 2010, Online Course long form notes (including self assessment questions) and class notes (slides),
Richard Szeliski: 0, Computer Vision: Algorithms and Applications, Springer,
Paul F. Whelan, Derek Molloy: 0, Machine Vision Algorithms in Java, Springer,
E. R. Davies: 0, Machine Vision, Elsevier,

Articles:
None

Other Resources

1: Lecturer Website, paulwhelan.eu,
415237: 1, Module Website, EE425,
https://www.eeng.dcu.ie/~whelanp/ipa/ipa_notes.html: