Research and development of visual processing for low vision devices and the bionic eye
In 2004 there were 50,000 people legally blind in Australia, with numbers expected to increase to 87,000 by 2024 with the ageing population ("Eye Research Australia Clear Insight: The Economic Impact and Cost of Vision Loss in Australia" by Centre for Eye Research Australia).
Although there are many useful devices on the market to assist individuals with vision impairment, there is a lack of the type of sensor-based assistant systems that are newly appearing in cars (.e.g, lane departure, collision warning, navigation).
This project aims to develop a new generation of assistive devices (stand-alone wearable devices that individuals can without medical intervention) based on computer vision processing: it will produce prototype devices that aim to demonstrate effective assistance for individuals with vision impairment.
VIBE is also contributing expertise in computer vision processing to Bionic Vision Australia which is funded by the Australian Research Council. The Bionic Vision Australia partnership aims to build the first Australian bionic eye implant whereby individuals may recover some of the lost vision via electrical stimulation of the retina. Vision processing will be one of the key components of a bionic eye as it will enable efficient encoding of high resolution images into a set of
stimulation signals on a retinal implant.
Research Outcomes
Those may be described as improving/restoring
a) the ambulatory navigation (e.g. obstacle avoidance)
b) the interaction with the environment (e.g. face detection and recognition, reading of text/symbols, etc)
Who will benefit?
Individuals who have little or no vision as a consequence of diseases like Retinitis Pigmentosa (RP) and age-related macular degeneration (AMD).
Key Features
- Vision Processing
- computation of stable depth maps, free space encoding for ambulatory navigation
- face detection and recognition and general object detection and recognition
- Visual Neuroscience
- building a retinal model for simulation of the visual function
- building a visual cortex model for simulation of perception
Results to Date
We have developed new camera ego-motion estimation algorithms by using a multi-camera rig. The new algorithms are able to accurately and reliably compute the moving camera rig's instantaneous position and orientation though the camera observing its dynamic environment.
Further, we have developed efficient face detection algorithms which outperform the state-of-the-art (see paper by Shen, Welsh and Wang below). We are currently working on robust face recognition.
Research Team
Dr Nick Barnes (project leader), Manfred Doudar, Professor Richard Hartley, Dr Hongdong Li, Dr Paulette Lieby, Dr Nianjun Liu, Chris McCarthy, Dr Abd-Krim Seghouane, Dr Chunhua Shen.
PhD students: Markus Brenner, Yuchao Dai, Junae Kim, Jaekwang Lee, Hanxi Li, John Lim, Peng Wang, Di Yang.
Expressions of Interest
If you consider doing your PhD with us, contact Dr Paulette Lieby. Information about the NICTA enhanced PhD program may be found here.
Publications (subset)
Best paper: John Lim, Nick Barnes, Robust Visual Homing with Landmark Angles, In Proc Robotics Science and Systems (RSS), June 2009, Seattle, USA.
Hongdong Li, Efficient Reduction of L-infinity geometry problems, In proc IEEE CVPR 2009, June, Miami, USA.
Sakrapee Paisitkriangkrai, Chunhua Shen, Efficiently Training a Better Visual Detector with Sparse Eigenvectors, In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09), June, Miami, USA.