Course Details

Course Code COMP9517
Course Title Computer Vision
Units of Credit 6
Course Website
Handbook Entry

Course Summary

The course will cover topics from: Introduction, image processing, feature detection and matching, feature-based alignment, image stitching, segmentation, feature tracking, recognition, motion estimation, stereo correspondence and 3-D reconstruction, applications.

Course Timetable

The course timetable is available here .

Course Aims

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.

Student Learning Outcomes

After completing this course, students will:

  • know the foundations of scientific, statistical and engineering approaches to computer vision
  • learn to implement and test some computer vision algorithms
  • learn to appreciate the building and integration of large vision applications

This course contributes to the development of the following graduate capabilities:

Graduate Capability Acquired in
the skills involved in scholarly enquiry
Assignment, Project
an in-depth engagement with relevant disciplinary knowledge in its interdisciplinary context
the capacity for analytical and critical thinking and for creative problem solving
Test, Project
the ability to engage in independent and reflective learning Assignment, Project
the skills to locate, evaluate and use relevant information (Information Literacy) Test, Project
the capacity for enterprise, initiative and creativity
the skills required for collaborative and multidisciplinary work
the skills of effective communication
Project Presentation, Demo, Report; Assignment Report

Assumed Knowledge

Before commencing this course, students should:

  • know to program well in any of C/C++, Java, Python, or be willing to learn it independently
  • be familiar with data structures and algorithms, and basic statistics
  • be able to learn to use and integrate software packages
  • be familiar with vector calculus and linear algebra, or be willing to learn them independently

Teaching Rationale

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.

The compulsory lab session will provide an opportunity for evaluation and feedback.

Teaching Strategies

  • Lectures ... introduce concepts, show examples
  • Lab sessions... will be used for evaluation and feedback
  • Assignment .. allows students to solve a significant problem early and quickly
  • Mid-term test ... allow students to test subject knowledge acquired
  • Large group project... allows students to work in teams and build a significant computer vision application


Assessment Type Marks Release Date Due in
Assignment 10% Week 2 Week 4
Mid-term test 30% Week 9
Project (multiple stages): 50% (in total):
Stage 1 10% Week 4 Week 7
Specs of stage 2 10% Week 7 Week 10
Demo 10% Week 13
Performance evaluation (during demo and in report) 10%
Report 10% Fri of Week 13
Attendance at labs 10%

Academic Honesty and Plagiarism

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.

Course Schedule

Week Lecture Topic Lecturer
1 Introduction to Digital Imaging Arcot Sowmya
2 Image Processing Arcot Sowmya
3 Feature Detection and Matching Arcot Sowmya
4 Feature-based Alignment and Image Stitching Arcot Sowmya
5 Pattern Recognition Peter Cai
6 Segmentation Peter Cai
7 Feature Tracking Peter Cai
8 Object Recognition Peter Cai
9 Class test Arcot Sowmya
10 Project Stage 2 Specification Arcot Sowmya
11 3D Vision Yang Wang/Arcot Sowmya
12 Tracking and Temporal Models Yang Wang/Arcot Sowmya
13 Project Demo Arcot Sowmya

Resources for Students

Texts and recommended readings:

  • Textbook Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2011 (electronic version available: )
  • Reference Books
  • 1. Linda G. Shapiro and George C. Stockman, Computer Vision, Prentice Hall, 2001
  • 2. Forsyth, D. and Ponce, J. , Computer Vision: A modern approach, Prentice Hall, 2nd edition, 2011
  • 3. Ballard, D. H. and Brown, C. M., Computer Vision, Prentice Hall, New Jersey, 1982
  • 4. Gonzalez, R. C. and Woods, R. E., Digital Image Processing, Addison Wesley, Sydney, 3rd edition, 2008
  • 5. Sonka, M., Hlavac, V. and Boyle, R., Image Processing, Analysis and Machine Vision, Chapman and Hall, Melbourne, 3rd edition, 2007
  • 6. Duda, R. O., Stork, D. and Hart, P., Pattern Classification, John Wiley and Sons, 2000
  • 7. Medioni, G. and Kang, S. B., Emerging Topics in Computer Vision, Prentice Hall, NJ, 2005
  • 8. Simon J. D. Prince, Computer Vision: models, learning and inference, Cambridge University Press, 2012 , (electronic version available: )

Course Evaluation and Development

This course is evaluated each session using the CATEI system.

Based on feedback, the following changes are being made in this offering:

1. An experienced co-lecturer is now available and will co-teach the course.

2. More training for tutors will be provided, to improve the lab experience, feedback on assignments and with programming.

3. The marking criteria will be released before the assessments.

4. To better guide the group project, lab mentors will be assigned to specific groups.

5. Consultation on use of software packages will be encouraged during lab time.

Resource created Thursday 21 July 2016, 10:55:26 AM, last modified Friday 21 July 2017, 04:36:06 PM.

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