Module Details
| Module Code: |
COMP C4602 |
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Module Title:
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Computer Vision
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Title:
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Computer Vision
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| Module Level:: |
8 |
| Module Coordinator: |
Frances Hardiman
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| Module Author:: |
James Garland
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| Module Description: |
Computer vision has become commonplace in applications ranging from search to medical application and self-driving cars. This module shall investigate how images are acquired and information extracted by the computer using classical algorithms. The module shall cover how computers represent objects and their alignment and allow students to locate and track feature movement between images.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Assemble an image acquisition system, demonstrating an understanding of its constituent components. |
| LO2 |
Design an image acquisition system to demonstrate an understanding of enhancement and pattern matching within images. |
| LO3 |
Demonstrate the use of algorithms to track feature movement and displacement between frames of images. |
| LO4 |
Collect depth information from multiple (stereo) images and track the location of the feature in the z-plane. |
| LO5 |
Complete a project as an individual or in a small group to design and implement a solution for a real world problem. |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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| No recommendations listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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| Indicative Content |
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Acquisition system
Image acquisition system using COTS components. Camera, lenses and lens distortion, focal length, aperture, depth of field, exposure, shutter speed, frame rate affect on quality of the image acquisition. Improvements to image acquisition using passive and active lighting, flashes, radiometry.
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Image Enhancement
Introduction to image enhancement in both the spatial and frequency domains. Contrast enhancement and transformations. Histogram processing. Filtering.
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Pattern matching
Image convolution and feature detection, e.g. detection of edges and identifying features. Application of feature detectors and descriptors such as MOG, HOG, SIFT, SURF etc.
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Feature movement
Track the direction of feature movement using motion estimation, alignment, parametric and layered motion, etc.
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Depth interpolation
Extract depth information using, e.g. epipolar geometry techniques and show different styles of correspondence (dense, sparse) to interpret the depth of a set of images.
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Ethics and Safety
Ethical use and bias in captured data, reliable use of computer vision in safety systems
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Module Content & Assessment
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| Assessment Breakdown | % |
|---|
| Continuous Assessment | 20.00% |
| Project | 40.00% |
| Practical | 40.00% |
AssessmentsFull Time
| No End of Module Formal Examination |
| Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Workload Category |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
|
Contact |
No Description |
Every Week |
2.00 |
2 |
| Laboratory |
|
Contact |
No Description |
Every Week |
3.00 |
3 |
| Independent Learning |
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Non Contact |
No Description |
Every Week |
5.00 |
5 |
| Total Weekly Contact Hours |
5.00 |
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Module Resources
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| Recommended Book Resources |
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Szeliski, R. (2021), Computer Vision: Algorithms and Applications, 2.
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Prince, S. (2012), Computer Vision: Models, Learning, and Inference, Cambridge University Press.
| | This module does not have any article/paper resources |
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| This module does not have any other resources |
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