Use the Tab and Up, Down arrow keys to select menu items.
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.
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).
The topics studied in this course will include: Image processingDeep learningFiltering, Image Representations, and Texture Models Image registration and mosaicsColour Vision Neurophysiology of visionMulti-view Geometry Projective Reconstruction Stereo visionBayesian Vision; Statistical Classifiers Clustering & Segmentation; Voting Methods Invariant local featuresObject recognitionMedical Imaging Image Databases Motion interpretationTracking and Density Propagation Biometric authenticationHuman activity recognitionVisual Surveillance and Activity Monitoring Real-time robot vision (for robots and drones)Innovative computer vision based human-computer interfaces
Subject to approval of the Head of Department.
COSC428
Please note that the course activity times advertised here are currently in draft form, to be finalised on Monday 30 January 2023 for S1 and whole year courses, and Monday 26 June 2023 for S2 courses. Please hold off enquiries about these times until those finalisation dates.
Richard Green
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 ProjectYou 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 documentation3. Demonstration of your project (where demos are expected to match your conference paper results). ExamOur (40%) COSC428 two-hour test is CLOSED BOOK.Updated Semester One 2020 assessment deadlines and details will be available once finalised.
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.
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 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.
Domestic fee $1,079.00
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 .