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This course covers advanced techniques and algorithms used in real-time 3D computer vision and image processing, from medical imaging to intelligent autonomous UAV/robot vision.
The goal of computer vision (machine vision/robot vision) 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.The objective of this course is to present an insight into the world of Computer Vision (CV) that goes beyond image processing algorithms. Students will acquire knowledge and an understanding of computer 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.This course investigates current research topics in CV, with a focus on the methods that are used to develop applications. Recent research papers in the area will be reviewed. Students will carry out a small research project in a CV topic. Their project will be presented in the form of a paper suitable for submission to a research conference. This course encompasses the theory and practical applications of CV including image processing (useful in early stages of CV, usually to enhance particular information and suppress noise) and visual cognition (computational models of human vision) – from medical imaging to intelligent autonomous drone/robot vision.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 either C, C++, MATLAB, Python, C# or Java (on Windows, Linux, macOS, iOS or Android).
This course is intended for honours and graduate students, researchers, and practitioners interested in Computer Vision, where major areas will be introduced by the lecturer.After completing this course, successful students:are able to understand and explain a range of empirical methods used for conducting research and development in computer visionare able to understand and explain critical factors that should be considered during experimental designare able to develop research hypothesesare able to develop and deploy experiments that test research hypothesesare able to read and critically summarize in writing recent research papers
Subject to approval of the Head of Department.
For further information see Computer Science and Software Engineering Head of Department
COSC428 assessment items are:50%: research project presented as a conference style paper, including relevant literature review10%: class participation/presentations40%: final two hour exam (closed book)Research Project: You will decide on a research topic, in consultation with the course coordinator, early in the course. This computer vision project is evaluated by the quality of a six page conference style paper (not more than 4000 words), that describes the work. All COSC428 students can request 24/7 access to the computer vision lab in Erskine room 234.Your research project consists of:• Final conference ready paper.• Commented documented source code (which you authored) and associated documentation• Demonstration of your project (where demos are expected to match your conference paper results).
Materials for the course will be made available on Learn.
Course Information on Learn
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 B- 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.
Domestic fee $963.00
International Postgraduate fees
* 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 .