Undergraduate Scholarships

A few projects in computer vision are offered this year.  Some of of them may be jointly supervised with staff members of the School.  If you have in mind a computer vision research topic that is not listed below, I would be interested to hear from you.  Please note that I can only supervise up to 3 projects in any one year.

With appropriate adjustment, any of the projects below could be suitable for a BE(SE) final year project (12 points), an Honours Research Project (24 points), or a MSc project (24 points).

Experience has shown that it can be very beneficial for research students to have a group of people with related interests to share ideas with. A student undertaking any of the projects below is expected to join the Computer Vision Research Group and will be expected to attend and contribute to group meetings and discussions. Such a student will be housed in the Computer Vision Research Group Laboratory in Room 2.09 of the Computer Science building. You are also strongly advised to take the CITS4240 Computer Vision unit offered in the first semester.

My past final-year project students:

• Tyson STOLARSKI (2010, CEED project, Mechatronics Engineering), "Implementation of an Augmented Reality Visualisation System for Industrial Automation"
• Calin BORCEAU (2010, Mechatronics Engineering), "An Extensible Platform for Real-Time Monocular SLAM"
• Evgeni SERGEEV (2009, Honours), "Tracking Boundaries in Video via Segmentation"
• Michael GOOLD (2009, Mechantronics Engineering), "Recognising Guitar Chords in Real-Time"
• Barry VAN OUDTSHOORN (2008, Honours), "Investigating the Feasibility of Near Real-Time Music Transcription on Mobile Devices" (WAITTA finalist)
  
• Lih Wern HIEW (2007, Mechatronics Engineering), "Adaptive background modelling for motion tracking"
• Eko Kumiawan TENGGARA (2007, Honours), "Human Head Tracking in Cluttered Scenes"
• Daniel DELUCA-CARDILLO (2006, Honours), "3D Pose Recovery for the Human Arm" (WAITTA finalist)
• Robert BUDIMAN (2005, Honours), "Implementing motion capture for 3D cartoon movies"
• Chi Chiu CHENG (2003, MSc), "Scanner Video Mosaicing"
• John DARRINGTON (2002, Honours), "Character recognition using a multilayer perceptron classifier and orthogonal moments on the unit disc" (at Murdoch University)

1. Computer Animation.
   This project (general description) is likely to be split into 2-3 smaller Honours projects, each of which will include research components on motion capture, feature tracking,  stereo image analysis, and computer graphics.  Each of the smaller projects will be independent of each other and will be jointly supervised (to be finalized) by A. Prof. Mohammed Bennamoun, Dr. Peter Kovesi, Dr. E. J. Holden, and myself.   If you are interested, please contact Mohammed or myself.
   
   
   
2. Computer Vision for Vehicle Control.
   This project (description) will be jointly supervised by A. Prof. Mohammed Bennamoun and myself.  It might be more suitable for Mechatronics Engineering students.
   
   
   
3. Adaptive Background/Foreground Segmentation.
   This project studies the segmentation of foreground objects (e.g. people) in a dynamic, textured background from video sequences.  Examples of such time-varying texture backgrounds include changing illumination, swaying trees, waves on water, moving clouds, etc.
   
   Background/foreground segmentation has  extensive applications in movie editing, video surveillance, and image synthesis.  In movie editing, the images of the human actors are often required to be segmented from the background and then superimposed into different scenes; in video surveillance of a scene (e.g. train stations, indoor laboratories) over a long period of time, segmentation of the interesting objects, such as people and vehicles from a background under variable lighting conditions is often required; in image synthesis, moving objects often arise as outliers and their segmentation from the image sequences needs to be integrated with the estimation of camera geometry.
   
   
   
4. Image Smoothing using a Level Set Method.
   If an image is magnified by simply enlarging the image pixels then a result known as the blocking effect is evident.  To avoid this problem, a standard image processing technique, such as bilinear or bicubic interpolation, is often applied to improve the smoothness of the enlarged image. In this project, a level set method for image smoothing will be investigated.
   
   
   
5. Temporal-Spatial Reasoning on Weather Radar Images.
   This project is about temporal-spatial reasoning with incomplete information, in this case weather radar images, to predict time of rain front hitting a certain point within the metro area. This project will be a joint supervision with A. Prof. Mark Reynolds.
   
   Some useful links:
   A brief introduction to the field
   Current weather radar images in Perth
   
   
   

Return to the 4th year project list