| Course Code | COMP9517 |
| Course Title | Computer Vision |
| Units of Credit | 6 |
| Course Website | http://cse.unsw.edu.au/~cs9517 |
| Handbook Entry | http://www.handbook.unsw.edu.au/postgraduate/courses/current/COMP9517.html |
The course will cover topics from: Introduction, image processing, feature detection and matching, feature-based alignment, image stitching, pattern recognition, deep learning , segmentation, motion and image tracking, recognition, stereo correspondence and 3-D vision, applications.
The course timetable is available here .
Computer vision develops mathematical techniques for recovering three-dimensional shape and appearance of objects in images (Szeliski, 2010). Vision is difficult because it is an inverse problem, where only insufficient information is available when trying to recover some unknowns. Physics-based and statistical models are used to assist in the task. Current real-world applications are wide-ranging, and include optical character recognition, machine inspection, objection recognition in retail, 3-D model building in photogrammetry, medical imaging, automotive safety, match move in Hollywood (merging computer generated imagery with live action footage), motion capture, surveillance, fingerprint recognition and biometrics and others. This course will provide an introduction to fundamental concepts and an opportunity to develop an application.
After completing this course, students will:
This course contributes to the development of the following graduate capabilities:
| Graduate Capability | Acquired in |
|
the skills involved in scholarly enquiry
|
Assignments, Project
|
|
an in-depth engagement with relevant
disciplinary knowledge in its interdisciplinary context
|
Project
|
|
the capacity for analytical and critical
thinking and for creative problem solving
|
Tests, Project
|
| the ability to engage in independent and reflective learning |
Assignments, Project
|
| the skills to locate, evaluate and use relevant information (Information Literacy) | Tests, Project |
|
the capacity for enterprise, initiative and
creativity
|
Project |
|
the skills required for collaborative and
multidisciplinary work
|
Project |
|
the skills of effective communication
|
Project Presentation, Demo, Report
|
Before commencing this course, students should:
The principal mode of teaching is class lectures. Because of the volume of material available on the subject, lectures are better able to present high level overviews as well as in-depth presentation of selected topics.
Compulsory Lab sessions will be used to examine a method or algorithm in some detail, and provide an opportunity for evaluation and feedback.
| Assessment Type | Marks | Release Date | Due in |
| Assignment 1 | 10% | Week 2 | Week 4 |
| Assignment 2 | 15% | Week 4 | Week 6 |
| Test 1 | 20% | Week 7 | |
| Test 2 | 20% | Week 11 | |
| Project (multiple stages)** | 30% | Week 6 | |
| • Interim Project Presentation |
|
|
Week 8 |
| • Demo |
|
Week 13 | |
| • Project report |
|
Fri of Week 13 | |
| Lab mark | 5% |
The Student Code of Conduct ( Information , Policy ) sets out what the University expects from students as members of the UNSW community. As well as the learning, teaching and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.
In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code Policy as discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.
If you have any concerns, you may raise them with your lecturer, or approach the School Ethics Officer , Grievance Officer , or one of the student representatives.
Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:
Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible that your assignment files are not accessible by anyone but you by setting the correct permissions in your CSE directory and code repository, if using. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is defined as using the words or ideas of others and passing them off as your own.
If you haven't done so yet, please take the time to read the full text of
The pages below describe the policies and procedures in more detail:
You should also read the following page which describes your rights and responsibilities in the CSE context:
| Week | Lecture Topic | Lecturer |
|---|---|---|
| 1 | Introduction to Digital Images | Arcot Sowmya |
| 2 | Image Processing | Xiongcai Cai |
| 3 | Frequency based Techniques | Xiongcai Cai |
| 4 | Feature-based Alignment and Image Stitching | Xiongcai Cai |
| 5 | Pattern Recognition | Xiongcai Cai |
| 6 |
Deep Learning for Feature Extraction and Classification
|
Arcot Sowmya
|
| 7 |
Segmentation
|
Arcot Sowmya |
| 8 |
Motion and Image Tracking
|
Arcot Sowmya |
| 9 |
Project Presentations
|
Arcot Sowmya |
| 10 |
Recognition
|
Arcot Sowmya
|
| 11 | 3D Vision | Arcot Sowmya |
| 12 |
Applications
|
Arcot Sowmya
|
| 13 | Project Demos | Arcot Sowmya |
Texts and recommended readings:
This course is evaluated each session using the myExperience system.
Based on feedback, the following changes are being made in this offering:
1. To provide more coverage of concepts and algorithms, lab sessions have been redesigned.
2. To address marking consistency across markers, automarking of assignments will be trialled.
3. To provide more feedback, lab work will be assessed.
4. Frequent short quizes are being replaced by fewer but longer tests.
5. The number of reports will be reduced.
Resource created Friday 23 February 2018, 12:17:23 PM, last modified Friday 02 March 2018, 11:27:22 AM.