COMP07188 2020 Image Processing
Full Title
Image Processing
Transcript Title
Image Processing
Code
COMP07188
Attendance
N/A %
Subject Area
COMP - 0613 Computer Science
Department
COEL - Computing & Electronic Eng
Level
07 - Level 7
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Author(s)
Eva Murphy, Sean Mullery
Programme Membership
SG_EELEC_H08 202000 Bachelor of Engineering (Honours) in Electronics and Self Driving Technologies
SG_EROBO_H08 202000 Bachelor of Engineering (Honours) in Robotics and Automation
SG_EROBO_H08 202400 Bachelor of Engineering (Honours) in Robotics and Automation
SG_EROBO_H08 202500 Bachelor of Engineering (Honours) in Robotics and Automation
Description
This module presents the foundational image processing material that is required for diverse areas of Robotics, Remote Sensing, Computer Vision, Medical Imaging etc. It introduces what digital images are, how they are presented, how to analyse them and how to process them for specific use cases. Image Processing may be an end in itself, e.g. to present to a the image in a better form for human viewing, or as the first part of a computer vision pipeline.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Analyse the statistics of images
2.
Perform appropriate operations on images to achieve a desired result
3.
Enhance images with consideration of the ethical implications.
4.
Restore Images based on prior knowledge
5.
Segment images based on appropriate criteria.
Teaching and Learning Strategies
A lecture will be provided each week. In advance of the lecture, the learner may be asked to review key text book chapters that are relevant to the lecture so that they get the maximum learning from that lecture.
The Project work, will challenge the learner to master concepts beyond those covered in the theory lecture.
Module Assessment Strategies
A terminal exam and continuous assessment in the form of project work will be used to assess the module.
To reinforce the theoretical principles covered in lectures, learners will participate in project work.
The learner will complete a final exam at the end of the semester.
Repeat Assessments
Repeat Exams will be set for Autumn of each year.
Repeat project work can be submitted at the repeat exam sitting.
Indicative Syllabus
The sylabus will include a subset of the following to meet the learning outcomes
Image Formation: Images/formats, Grey - Colour, Colour Spaces
Analysis: Histograms
Point-wise operations: Brightness, Contrast, Gamma, Histogram Equalisation, Linear operations,
Convolution: Edge detection
Spatial transforms: Resizing, Nearest neighbour, Bilinear/Bicubic
Interpolation, Pyramids, Geometric Transformations
Image Enhancment (subjective): Noise reduction, Debluring, societal effects of Image Enhancement,
Ethics of Image Processing: Scientific data, Beautifying data.
Image Restoration (prior knowledge): reverse specific damage
Segmentation: Active Contours, Split and Merge, Mean shift, Normalised Cuts, Graph cuts and energy-based methods.
Colour image processing.
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
40 %
End of Semester / Year Formal Exam
60 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Written Exam | Coursework Assessment | Closed Book Exam | 20 % | Week 7 | 1,2,4 |
| 2 | Practical Work | Practical | Assignment | 20 % | OnGoing | 1,2,4,5 |
End of Semester / Year Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Written Exam | Final Exam | Closed Book Exam | 60 % | End of Semester | 1,2,3,4,5 |
Full Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Lecture Theatre | Lecture | 2 | Weekly | 2.00 |
| Practical / Laboratory | Computer Laboratory | Laboratory Practical | 2 | Weekly | 2.00 |
| Independent Learning | Not Specified | Independent Learning | 3 | Weekly | 3.00 |
Total Full Time Average Weekly Learner Contact Time 4.00 Hours
Online Learning Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Online | Lecture | 1.5 | Weekly | 1.50 |
| Practical / Laboratory | Online | Laboratory Practical | 1 | Fortnightly | 0.50 |
| Independent Learning | Not Specified | Independent Learning | 5 | Weekly | 5.00 |
Total Online Learning Average Weekly Learner Contact Time 2.00 Hours
Required & Recommended Book List
Required Reading
2010-10-19 Computer Vision Springer
ISBN 1848829345 ISBN-13 9781848829343
2010-10-19 Computer Vision Springer
ISBN 1848829345 ISBN-13 9781848829343
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
Recommended Reading
26/10/2019 Digital Image Processing, Global Edition Pearson;
26/10/2019 Digital Image Processing, Global Edition Pearson;
Recommended Reading
30/11/2018 Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data Packt Publishing
30/11/2018 Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data Packt Publishing