Registry

Module Specifications

Archived Version 2019 - 2020

Module Title	Image Processing and Analysis
Module Code	EE425
School	School of Electronic Engineering
Online Module Resources
Module Co-ordinator	Prof Paul F. Whelan	Office Number	S362
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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 [including: long-format electronic notes and associated course text, pdf versions of the class slides, lecture screencasts, image analysis development environment (used for the assignments and to illustrate computer vision concepts), self assessment questions and selected examples illustrating key concepts].
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

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 Matlab 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)

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: 2010, Online Course long form notes (including self assessment questions) and class notes (slides),
Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins: 0, Digital Image processing using MATLAB, Gatesmark Publishing,
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

30469, Lecturer Website, 0, paulwhelan.eu, http://paulwhelan.eu/, 30470, Module Website, 0, EE425, https://www.eeng.dcu.ie/~whelanp/ipa/ipa_notes.html,

Programme or List of Programmes

AP	BSc in Applied Physics
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)
ME	B.Eng. in Mechatronic Engineering
PBM	BSc Physics with Biomedical Sciences
PHA	BSc in Physics with Astronomy

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