COSC428-22S1 (C) Semester One 2022

Computer Vision

15 points

Details:
Start Date: Monday, 21 February 2022
End Date: Sunday, 26 June 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 6 March 2022
  • Without academic penalty (including no fee refund): Sunday, 15 May 2022

Description

This course covers advanced techniques and algorithms used in real-time 3D computer vision, image processing and deep learning - from medical imaging to intelligent autonomous UAV/robot vision.

2022 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.

The goal of computer vision/machine vision/robot vision/drone vision and deep learning is to recognise objects and their motion by creating a model of the real world from images. Object recognition and tracking needs to allow for large variations in appearance caused by changes in viewing position, illumination, occlusion and object shape.

This course encompasses the theory and practical applications of computer vision including image processing (useful in early stages of computer vision, usually to enhance particular information and suppress noise) and visual cognition (computational models of human vision) – from medical imaging to intelligent autonomous UAV/robot vision.

The objective of this course is to present an insight into the world of computer vision that goes beyond image processing algorithms. Students will acquire knowledge and an understanding of artificial vision from a system’s viewpoint. Various aspects will be examined and the main approaches currently available in the literature will be discussed, opening the door to the most important research themes.

COSC428 is available to all computer science, computer engineering, mechatronics, electrical engineering and software engineering students enrolled in their fourth year. The mathematical nature of computer vision enables the course material to be pitched as algorithms to computer science students and mathematics to engineering students (images are 2D matrices after all) and students have the option of completing their projects using any programming language/script (such as Python, C, C++, MATLAB, C#, Java) on any operating system (such as Windows, Linux, macOS, iOS or Android).

Learning Outcomes

  • The topics studied in this course will include:
  • Image processing
  • Deep learning
  • Filtering, Image Representations, and Texture Models
  • Image registration and mosaics
  • Colour Vision
  • Neurophysiology of vision
  • Multi-view Geometry
  • Projective Reconstruction
  • Stereo vision
  • Bayesian Vision; Statistical Classifiers
  • Clustering & Segmentation; Voting Methods
  • Invariant local features
  • Object recognition
  • Medical Imaging
  • Image Databases
  • Motion interpretation
  • Tracking and Density Propagation
  • Biometric authentication
  • Human activity recognition
  • Visual Surveillance and Activity Monitoring
  • Real-time robot vision (for robots and drones)
  • Innovative computer vision based human-computer interfaces

Prerequisites

Subject to approval of the Head of Department.

Equivalent Courses

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 14:00 - 16:00 E7 Lecture Theatre
21 Feb - 10 Apr
2 May - 5 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Monday 10:00 - 12:00 Jack Erskine 134 Lab 3
21 Feb - 10 Apr
2 May - 5 Jun
02 Tuesday 15:00 - 17:00 Jack Erskine 134 Lab 3
21 Feb - 10 Apr
2 May - 5 Jun
03 Thursday 08:00 - 10:00 Jack Erskine 134 Lab 3
21 Feb - 10 Apr
2 May - 5 Jun
04 Thursday 13:00 - 15:00 Jack Erskine 134 Lab 3
21 Feb - 10 Apr
2 May - 5 Jun

Timetable Note

Please note that the course activity times advertised here are currently in draft form, to be finalised on Monday 31 January 2022 for S1 and whole year courses, and Monday 27 June 2022 for S2 courses. Please do hold off enquiries about these times till those finalisation dates.

Course Coordinator

Richard Green

Assessment

Assessment Due Date Percentage 
Research Project 50%
Participation 10%
Final Exam (during the examinations period) 40%


2022 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.

Research Project
You will decide on a research topic, in consultation with Richard Green, early in the course. This computer vision project is evaluated by the quality of a 6 page conference style paper (not more than 4000 words), that describes your work . Depending on project choice, COSC428 students can access the computer vision lab in Erskine room 234.

Your research project consists of:
1. Final conference ready paper.
2. Commented documented source code (which you authored) and associated documentation
3. Demonstration of your project (where demos are expected to match your conference paper results).


Exam
Our (40%) COSC428 two-hour test is CLOSED BOOK.

Updated Semester One 2020 assessment deadlines and details will be available once finalised.

Textbooks / Resources

1. “Computer Vision, A Modern Approach”, by D.A. Forsyth & J. Ponce, Prentice Hall.
2. “Machine Vision”, by R. Jain, R. Kasturi, B. G. Schunck, McGraw Hill.
3. “Learning OpenCV: Computer Vision with the OpenCV Library”, by Gary Rost Bradski, Adrian Kaehler.

Additional Course Outline Information

Grade moderation

The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:
1. You must achieve an average grade of at least 50% over all assessment items.
2. You must achieve an average mark of at least 45% on invigilated assessment items.

If you satisfy both these criteria, your grade will be determined by the following University-wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a C+ grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.

Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control.

Applications for special consideration should be submitted via the Examinations Office website within five days of the assessment.

Where an extension may be granted for an assessment, this will be decided by direct application to the Department and an application to the Examinations Office may not be required.

Special consideration is not available for items worth less than 10% of the course.

Students prevented by extenuating circumstances from completing the course after the final date for withdrawing, may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the end of the main examination period for the semester.

Indicative Fees

Domestic fee $1,051.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Computer Science and Software Engineering .

All COSC428 Occurrences

  • COSC428-22S1 (C) Semester One 2022