This has been replaced by the 6 Research Theme Categories.
Congratulations to Zifeng and Chunhua on having made it to the top of the Cityscapes leaderboard again. Cityscapes is a semantic segmentation dataset of city scenes, and a hotly contested international challenge. The challenge is to separate the pixels belonging to different classes of objects. Semantic Segmentation is one of the fundamental challenges in computer […]
We just had a piece on medical machine learning published in the Conversation.
The ACVT has been working with LBT Innovations, a South Australian medical device company, for more than 5 years on a new form of medical device to automate the reading of Agar plates. The processing of Agar plates is an important and time-consuming part of the normal operation of hospitals and clinics around the world. […]
We’ve had another great year in the ImageNet competition. We came 2nd in the Scene Parsing challenge, which requires pixelwise segmentation of a large set of images into 150 classes of things and stuff. The ImageNet Challenge is one of the most hotly contested challenges in Computer Vision, and is constantly updated to reflect the current […]
From the LBT annual report: A ten-week pivotal clinical trial at TriCore Reference Laboratories in New Mexico during July and August 2015 tested APAS against a panel of microbiologists. Culture plates from 5,500 patients were processed by APAS and simultaneously assessed by a panel of independent qualified microbiologists. The results showed that APAS achieved over […]
John Bastian and Anton van den Hengel are among the authors of a new paper just published in Nature Scientific Reports. The paper describes a Machine Learning-based approach to generating Bose-Einstein Condensates (BECs). One of the difficulties in generating BECs is the need to cool Rubidium ions down to 10^-9 degrees Kelvin. This is achieved […]
The ImageNet Object Detection results are out, and we did extremely well! We place 4th, behind Microsoft, Qualcomm, and Chinese University of Hong Kong, but ahead of Google, Intel, and Tencent. This is all the more impressive as we had a fraction of the computing resources of these competitors.
Last week was the deadline for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2015) large-scale object detection task. This is the primary challenge for image-based object detection. The challenge requires that you detect 200 classes of objects in a set of test images. For each image, algorithms must produce a set of annotations (ci,si,bi)of […]
A research team (Dr. Guosheng Lin, Prof. Chunhua Shen, Prof. Ian Reid, Prof. Anton van den Hengel) at the School of Computer Science, The University of Adelaide developed innovative “Deep Structured Learning” techniques that set up the new state-of-the-art semantic image segmentation record in the PASCAL VOC Challenge, which is organised by the University of Oxford. The Adelaide team […]
In another indication that the Machine Learning behind most Computer Vision Problems has more general applicability, we have just had a paper accepted which shows that the approach we developed for pedestrian detection achieves the world’s best performance in predicting protein-protein interactions. This result is based on the data set labelled ‘Physical Interaction Task in […]