Category: Machine Learning
ACVT has strong capabilities in the field of machine learning and is developing new techniques and applying them to a range of diverse problem spaces. For example, work is underway in the area of automatic feature extraction and classification from satellite and aerial imagery using a supervised machine learning system. Of key importance is the development of robust classifiers and an associated framework by which to apply them to geospatial data. ACVT is also working on entity extraction and resolution from unstructured data sources. This involves the development of unsupervised clustering techniques which are able to be applied on a massive scale.
The ACVT had 12 papers accepted to IEEE Computer Vision and Pattern Recognition 2015 (CVPR’15), one of the top 2 conferences in Computer Vision, which must be an Australian record. In terms of h5-index, CVPR ranks as the 7th venue in all Engineering and Computer Science after Nano and Nature journals. To put this in […]
LBT Innovations (ASX:LBT) has published the details of its extensive study into the accuracy of its Automated Plate Assessment System (APAS). Results from the study were released in a poster presentation at the Australian Society for Microbiology annual meeting in Melbourne this week. The poster confirms the headline result that, comparing the screening of hundreds of clinical samples by APAS […]
PhD Scholarships in Visual SLAM and Computer/Robotic Vision and Machine Learning for Visual Recognition
The Australian Centre for Visual Technologies (ACVT) at The University of Adelaide has 12 PhD scholarships available for both local and international students interested in pursuing a PhD. These Scholarships are funded from an ARC Laureate Fellowship to Prof Ian Reid entitled “Lifelong Computer Vision Systems”, and an ARC Centre of Excellence in Robotic Vision, […]
PhD Research Scholarships Expressions of Interest for the Data to Decisions CRC PhD Research Scholarships are invited from outstanding students to undertake PhD studies with the Data to Decisions CRC (D2D CRC) at ACVT. D2D CRC PhD Research Scholarships have been established to encourage outstanding PhD students to undertake research programs in the areas of […]
ACVT researchers Prof. Ian Reid, Prof. Anton van den Hengel, Associate Prof. Chunhua Shen and Dr. Gustavo Carneiro are part of a team that has been awarded a prestigious ARC Centre of Excellence in Robotic Vision. The Centre has received $19M of funding and will conduct a 7-year program of research with the following focus […]
Dr Gustavo Carniero has won a 3 year ARC Discovery Grant valued at $295,000. The project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a […]
ACVT has been awarded a 3 year ARC Discovery Grant valued at $380,787. The project will be led by Dr Qinfeng Shi and will include collaboration with Charles Sturt University and the University of Southampton. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in […]
Lusia Guthrie was interviewed by Amanda Vanstone about our collaboration on the ABC’s Counterpoint program. The ex Foreign Minster was particularly pleased that the technology was developed in South Australia. The audio is available here.
ACVT has been awarded a 3 year ARC Linkage Project valued at $377,000. The industry partner is BAE Systems. The CIs on the project are Prof. Anton van den Hengel, Dr Chunhua Shen, Dr Anders Eriksson and Dr Qinfeng Shi. Identifying whether there has been a significant change in a scene from a set of […]
ACVT has been awarded $210,000 through the ARC Linkage Infrastructure and Equipment Facilities (LIEF) program to further develop its Machine Learning capacity. Machine learning is responsible for many recent advances in image-based information analysis, from finding minerals in satellite images, to image-based guidance of autonomous vehicles. This progress is due to new methods for learning […]