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This course covers advanced techniques and algorithms used in real-time computer vision and image processing design.
The goal of computer 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. 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). 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.
The topics studied in this course will include: Image processing 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 vision Innovative interfaces
Subject to approval of the Head of Department.
Richard Green
There will be a research project presented as a conference style paper (50%) and reviews of selected papers and/or class participation/presentations (10%). The final test (40%) will be used to evaluate a student’s overall understanding of the theoretical aspects discussed in the course.
1. “Computer Vision, A Modern Approach”, by D.A. Forsyth & J. Ponce, Prentice Hall, 2003. 2. “Machine Vision”, by R. Jain, R. Kasturi, B. G. Schunck, McGraw Hill, 1995. 3. “Learning OpenCV: Computer Vision with the OpenCV Library”, by Gary Rost Bradski, Adrian Kaehler, 2008
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. AegrotatsIf factors beyond your control (such as illness or family bereavement) prevent you from completing some item of course work (including laboratory sessions), or prevent you from giving your best, then you may be eligible for aegrotat, impaired performance consideration or an extension on the assessment. Details of these may be found in the University Calendar. Supporting evidence, such as a medical certificate, is normally required. If in doubt, talk to your lecturer.
Domestic fee $917.00
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 .