Courses

Current Computer Vision Courses


 

Individual Projects ENGN4200 (ANU)

Students undertake an individual engineering project, with supervision.

Students are encouraged to put forward their own ideas for the individual project, or they may select a project from a range of ideas offered by researchers across the ANU. If the student initiates an idea, he or she must find a supervisor to accept the project.

For more information about this course, click here.

Computer Vision ENGN4528 (ANU)

This subject introduces students to understanding of the fundamental problems in computer vision, and their state-of-the-art solutions. Topics covered in detail include: camera geometry, image formation, image filtering, thresholding and image segmentation, edge, point and line detection, geometric frameworks for vision, single view and two views geometry; 3D modelling and reconstruction, camera calibration; stereo vision, motion and optical flow; object recognition, appearance based scene recognition; pose estimation in perspective images, etc. The course is featured by an extensive practical component including computer labs and term projects that provides the students with a tool box of skills in image processing and computer vision.

For more information about this course, click here.

Computer Vision ENGN6528 (ANU)

This subject extends students comprehension of the fundemental problems of computer vision and state-of-the-art solutions. Topics covered in detail include: image acquisition, sampling and quantisation; image segmentation, point, line and edge detection, and thresholding; geometric frameworks for vision, single view and two views; camera calibration; stereopsis, the correspondence problem and epipolar geometry; motion and optical flow; recognition, invariants, appearance and geometric-based identification; pose estimation in perspective images. The course includes an extensive practical component that provides the students with a tool box of skills in image processing and computer vision.

For more information about this course, click here.

Computer Graphics COMP6461(ANU)

Computer graphics are an intrinsic component of many modern software applications and are often essential to the success of these applications. The objective of this course is to familiarise the student with fundamental algorithms and data structures that are used in today’s interactive graphics systems as well as programming and architecture of high-resolution graphics computers. The principles and practice of computer graphics are described from their mathematical foundations to the modern applications domains of scientific visualisation, virtual reality, computer games and film animation. The course will include some practical experience of graphical software environments such as OpenGL, JOGL, VRML and Java3D. Students should have a good working knowledge of Java, and of 3D coordinate geometry, before taking this course.

For more information about this course, click here.

Algorithms and Techniques for Data Mining COMP8400 (ANU)

Large amounts of data are increasingly being collected by public and private organisations, and research projects. Additionally, the Internet provides a very large source of information about almost every aspect of human life and society.
This course provided a practical focus on the technology and research in the area. It focuses on the algorithms and techniques and less on the mathematical and statistical foundations.

For more information about this course, click here.

High Performance Scientific Computing COMP6464 (ANU)

This course provides an introduction to High Performance Computing with an orientation towards applications in science and engineering. Aspects of numerical computing and the design and construction of sophisticated scientific software will be considered. The focus will be on the C and C++ programming languages, although reflecting the reality of modern scientific computation this course will also touch on other languages such as Python, Java and FORTRAN95. The course will study high performance computer architectures, including modern parallel processors, and will describe how an algorithm interacts with these architectures. It will also look at practical methods of estimating and measuring algorithm/architecture performance.

For more information about this course, click here.

Artificial Intelligence COMP6320 (ANU)

Artificial intelligence is the science that studies and develops methods of making computers more /intelligent/. The focus of this course is on core AI techniques for knowledge representation, search, reasoning, learning and designing intelligent agents. The course also aims to give an overview of other topics within AI, such as for example robotics, and of the historical, philosophical, and logical foundations of AI.

For more information about this course, click here.

Document Analysis COMP6490 (ANU)

Processing of semi-structured documents such as internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the \document” and its various genres as a fundamental object for business, government and community. For this, the course covers four broad areas: (A) information retrieval, (B) natural language processing, (C) machine learning for documents, and (D) relevant tools for the Web. Basic tasks here are covered including content collection and extraction, formal and informal natural language processing, information extraction, information retrieval, classification and analysis. Fundamental probabilistic techniques for performing these tasks, and some common software systems will be covered, though no area will be covered in any depth.

For more information about this course, click here.

Biomedical Imaging ENGN3820 (ANU)

This course covers the physical concept behind the bio-signal transduction that leads to the acquisition of signals from a biological system. It will also provide a framework towards the understanding of interpretation of these signals into multi-dimensional data for imaging and analysis. The images or signal are then used for understanding biological systems and also diagnostics purposes. Key concept behind these techniques such as MRI, ultrasound, biophotonics, microscopy, EEG, and medical imaging processing will be taught in this course. There will be examples of qualitative performance in the context of molecular and clinical settings.

For more information about this course, click here.