M
Michael Blumenstein
Researcher at University of Technology, Sydney
Publications - 343
Citations - 5826
Michael Blumenstein is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Feature extraction & Handwriting recognition. The author has an hindex of 37, co-authored 328 publications receiving 4764 citations. Previous affiliations of Michael Blumenstein include Commonwealth Scientific and Industrial Research Organisation & Australian Artificial Intelligence Institute.
Papers
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Proceedings ArticleDOI
A study on detecting drones using deep convolutional neural networks
TL;DR: The experimental results show that VGG16 with Faster R-CNN perform better than other architectures on the training dataset, and visual analysis of the test dataset is also presented.
Proceedings ArticleDOI
A novel feature extraction technique for the recognition of segmented handwritten characters
TL;DR: This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system.
Journal ArticleDOI
A Decade of Research on the Use of Three-Dimensional Virtual Worlds in Health Care: A Systematic Literature Review
TL;DR: The big picture of application areas of 3DVWs presented in this review could be of value and offer insights to both the health care community and researchers.
Proceedings ArticleDOI
Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
Marcus Liwicki,Muhammad Imran Malik,C. Elisa van den Heuvel,Xiaohong Chen,Charles E.H. Berger,Reinoud D. Stoel,Michael Blumenstein,Bryan Found +7 more
TL;DR: A signature verification competition on datasets with two scripts (Dutch and Chinese) in which questions were asked to compare questioned signatures against a set of reference signatures and methods used by Forensic Handwriting Examiners (FHEs) were applied.
Proceedings ArticleDOI
Recent advances in video-based human action recognition using deep learning: A review
TL;DR: This paper presents a review of various state-of-the-art deep learning-based techniques proposed for human action recognition on three types of datasets, namely, single viewpoint, multiple viewpoint and RGB-depth videos.