Note: This unit version is currently being edited and is subject to change!
AMME4710: Computer Vision and Image Processing (2019 - Semester 2)
| Unit: | AMME4710: Computer Vision and Image Processing (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Senior Advanced |
| Faculty/School: | School of Aerospace, Mechanical & Mechatronic Engineering |
| Unit Coordinator/s: |
Dr Bryson, Mitch
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| Session options: | Semester 2 |
| Versions for this Unit: | |
| Site(s) for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | MTRX3700 OR MECH4720 OR MECH5720. |
| Brief Handbook Description: | This unit of study introduces students to vision sensors, computer vision analysis and digital image processing. Students will learn about fundamental algorithms in computer vision and be exposed to state-of-the-art developments in computer vision research. Students will explore the fundamental difficulties in automated reasoning from image data and learn key skills and practical knowledge in designing solutions to engineering problems involving vision. This course will introduce students to the fundamental principles of image sensor architectures, radiometry, colour reconstruction and projective geometry. Students will gain knowledge and skills in developing automated approaches to image analysis, segmentation, object recognition, classification and image enhancement. Students will learn about approaches to radiometric and projective calibration of camera systems, stereo imaging, image-based navigation and the estimation of 3D scene parameters from images. |
| Assumed Knowledge: | The unit assumes that students have strong skills in MATLAB. |
| Lecturer/s: |
Dr Bryson, Mitch
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| Tutor/s: | Fredrik Westling | |||||||||||||||
| Timetable: | AMME4710 Timetable | |||||||||||||||
| Time Commitment: |
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| T&L Activities: | Lectures are used to present new content. Laboratory activities are based in a computer lab and are used to complete practicals/assignments and to work on the final group design project. |
Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.
(6) Communication and Inquiry/ Research (Level 4)
1. Develop skills in presenting a final design solution to a computer vision/image processing problem
(7) Project and Team Skills (Level 3)
2. Develop skills in working on a design project within a team including communicating with team members, planning and managing tasks.
(4) Design (Level 4)
3. Design an engineering solution to a given image processing task by selecting and evaluating appropriate algorithms for a given image processing task.
(2) Engineering/ IT Specialisation (Level 4)
4. To develop an understanding of the fundamental principles of how images are formed including the basics of image sensors, radiometry, colour and projective geometry.
5. Apply basic techniques in image processing including the use of image filtering, features, edge detection, colour spaces/transforms and matching.
6. Apply advanced techniques in computer vision including stereo vision, 3D mapping, object detection, image classification and use of machine learning algorithms in vision
7. Apply a wide range of image processing techniques to real world applications
8. Understand the type of algorithm required for a particular image processing task
| Assessment Methods: |
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| Assessment Description: |
Students will work on specific tutorials during weeks 1, 2, 3, 5, 6 and 7 that will be marked. Tutorials will run during the remaining weeks and will be used to work on assignments and a major group design project. Students will work on two assignment tasks due week 4 and week 8. Students will spend the last 5 weeks of the course working in teams on a major design task. Teams will be assessed on a presentation given to the group during week 13 and a final design report due week 13. The University has authorised and mandated the use of text-based similarity detecting software Turnitin for all text-based written assignments. The majority of assessment tasks involve the use of MATLAB. The course assumes students have strong skills in MATLAB. |
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| Assessment Feedback: | Feedback on tutorials/assignments/final design project will be provided during the semester, and students will receive feedback from the teaching team and peers on their final design project presentation in week 13. | ||||||||||||||||||||||||||||||||||||
| Grading: |
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| Policies & Procedures: | See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies. |
| Prescribed Text/s: |
Note: Students are expected to have a personal copy of all books listed.
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| Recommended Reference/s: |
Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
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| Online Course Content: | Please see the AMME4710 Canvas site. |
Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
| Week | Description |
| Week 1 | Introduction / Digital Image Fundamentals |
| Week 2 | introduction to radiometry, colour, colour image processing, projective geometry. |
| Week 3 | Image filtering and edge detection |
| Week 4 | Image features, matching, correspondence and detection |
| Assessment Due: Assignment 1 * | |
| Week 5 | Stereo imaging, camera calibration, 2D/3D image projective relationships, image-based navigation |
| Week 6 | Image segmentation and clustering |
| Week 7 | Object recognition, image classification, introduction to machine learning |
| Week 8 | image classification and deep learning in computer vision |
| Assessment Due: Assignment 2 * | |
| Week 9 | Computer vision projects, software packages |
| Week 10 | Advanced image-based navigation, introduction to structure-from-motion, 3D image-based mapping |
| Week 11 | Advanced applications of computer vision: Face detection and recognition |
| Week 12 | Computer Vision Research Seminar |
| Week 13 | Project Presentations |
| Assessment Due: Project Presentation * | |
| Assessment Due: Project Final Report * | |
| STUVAC (Week 14) | N/A |
Course Relations
The following is a list of courses which have added this Unit to their structure.
Course Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| (6) Communication and Inquiry/ Research (Level 4) | No | 23.5% |
| (7) Project and Team Skills (Level 3) | No | 11.5% |
| (5) Interdisciplinary, Inclusiveness, Influence (Level 4) | No | 0% |
| (4) Design (Level 4) | No | 10% |
| (3) Problem Solving and Inventiveness (Level 4) | No | 0% |
| (2) Engineering/ IT Specialisation (Level 4) | No | 55% |
These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.
