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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Image Processing and Analysis
Module Code EE425
School School of Electronic Engineering
Module Co-ordinatorSemester 1: Paul Whelan
Semester 2: Paul Whelan
Autumn: Paul Whelan
Module TeachersPaul Whelan
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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.

Workload Full-time hours per semester
Type Hours Description
Lecture24This module is presented in a traditional format (lecture and continuous assessment) with significant practical support.
Laboratory12Laboratory support for coursework & tutorials
Independent Study89General revision and practice, Coursework, Online activity with module material. Homeworks and tutorials.
Total Workload: 125

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 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

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

• 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

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

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

Assessment Breakdown
Continuous Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Practical/skills evaluationPractical Assignments25%n/a
Reassessment Requirement Type
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: 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,
Other Resources

59364, Lecturer Website, 0, paulwhelan.eu, http://paulwhelan.eu/, 59365, Module Website, 0, EE425, https://www.eeng.dcu.ie/~whelanp/ipa/ipa_notes.html,
Programme or List of Programmes
APBSc in Applied Physics
ECSAStudy Abroad (Engineering & Computing)
ECSAOStudy Abroad (Engineering & Computing)
MEB.Eng. in Mechatronic Engineering
MEIMMEng in Mechatronic Engineering
MTBSc in Mechatronic Technology
PBMBSc Physics with Biomedical Sciences
PHABSc in Physics with Astronomy
Date of Last Revision09-JAN-06

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