Latest Module Specifications
Current Academic Year 2025 - 2026
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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 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. In addition to the common elements associated with EE425, this module will develop on these core ideas to expand into more advanced IPA engineering topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Recall, review and analyse the essential theories, algorithms, methodologies and techniques involved in basic and advanced computer vision. 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 computer vision techniques. 4. Apply computer vision techniques in a range of application scenarios. 5. Develop an understanding of the engineering issues involved in the commercial development of image processing and analysis solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 |
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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) Systems Engineering • Optical Terminology • Lenes & Filters • Monochrome Aberrations • Lighting Design • Image Sensors • Chromatic Aberrations • Pixel Level Effects 3D Imaging • Passive Stereoscopic Methods • Camera Calibration • Shape from Stereo • Image Rectification • Stereo Feature Matching • Colour / Multiple Views Stereo Vision • Active Stereoscopic Methods | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
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Other Resources
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