COMP08176 2020 Computer Vision
Full Title
Computer Vision
Transcript Title
Computer Vision
Code
COMP08176
Attendance
N/A %
Subject Area
COMP - Computing
Department
COEL - Computing & Electronic Eng
Level
08 - NFQ Level 8
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, Shane Gilroy, 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_EMECH_H08 202000 Bachelor of Engineering (Honours) in Mechanical Engineering
Description
This module presents the fundamental processes that allow computers to view and make sense of the world. Computer Vision includes both the physical hardware for acquiring the image, the environment in which the image is acquired and the classical algorithms for understanding the acquired image. This may be taken as a standalone module or in conjunction with an Image Processing and Deep Learning module to complete the low, mid and high-level computer vision domains.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Categorise and explain the various parts of an image acquisition system
2.
Identify and match features in corresponding images
3.
Re-construct 3D features from stereo images
4.
Track the movement of features between corresponding images
5.
Perform computational photography techniques on image sets.
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 syllabus will be a subset of the following to meet the learning outcomes
Image Acquisition: Cameras and Optics, Pin hole, Thin lens, Focal length, Composite lens, Distortions and aberrations, Aperture and Depth of field, Exposure, Shutter speed, Frame-rate, Types of Projection, Sensors, De-mosaicing, Processing Pipeline
Lighting: Radiometry, Light sources, Controlled lighting, Flash sync speed, Active Lighting
Feature detection: Points, Edges, Features
Lines: Hough transform, Projection onto line
Matching Features: RANSAC
Alignment: Stitching, Reprojection, Fish eye correction
Motion: Optic Flow, Translational alignment, Parametric Motion, Spline based motion, Layered motion
Stereo Correspondence: Epipolar Geometry, Sparse Correspondence, Dense Correspondence, Local methods, Global optimisation, Multi-view stereo
3D Reconstruction: Shape from X, Active Range finding
Feature Descriptors: HOG, SIFT, SURF
Computational Photography HDR, Super-resolution, Colourisation, Image matting and compositing, Multi-lens systems, Light field cameras
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 | Mid-term exam | Coursework Assessment | Closed Book Exam | 20 % | Week 7 | 1,2,3 |
| 2 | Laboratory Assignments | Practical | Assignment | 20 % | OnGoing | 2,3,4,5 |
End of Semester / Year Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | End of Term 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.
Required Reading
2017-10 Computer Vision Academic Press
ISBN 012809284X ISBN-13 9780128092842
2017-10 Computer Vision Academic Press
ISBN 012809284X ISBN-13 9780128092842
Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/ Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications-including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians-give the 'ins and outs' of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The 'recent developments' sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples-code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)