University of Canterbury

Computer Vision


Iowa State Course Substitution

ME Technical Elective


Course Info

International Credits: 15.0
Converted Credits: 4.0
Country: New Zealand
Language: English
Course Description:
COSC428-18S1 (C) Semester One 2018 Computer Vision 0.1250 EFTS 19 Feb 2018 - 24 Jun 2018 Description 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/drone 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) – 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 articial 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. Pre-requisites Subject to approval of the Head of Department. Course Coordinator For further information see Computer Science and Software Engineering( Head of Department Assessment 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 the work . All COSC428(GetCourseDetails.aspx? course=COSC428&year=2018) students will have access to 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). Textbooks 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 links Type Research Project Due Date Percentage 50% Type Class Participation Due Date Percentage 10% Type Final Exam Due Date Percentage 40% 10/6/2017 COSC428 - 18S1 (C) (2018): Computer Vision 2/3 Course Information on Learn ( Additional Course Outline Information Syllabus 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 Classiers • 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 Indicative Fees Domestic fee( $982.00 International fee( $4,363.00 * Fees include New Zealand GST 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 COSC428-18S1 (C) Semester One 2018 Previous year(/courseinfo/GetCourses.aspx?course=COSC428&year=2017&includeold=true) GENERAL ENQUIRIES +64 3 366 7001(tel:006433667001) ENROLMENT ENQUIRIES


Evaluation Date:
November 1, 2017
John Wagner