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.
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Journal ArticleDOI
Integrated intelligent water-energy metering systems and informatics: Visioning a digital multi-utility service provider
Rodney Anthony Stewart,Khoi Nguyen,Cara Beal,Hong Zhang,Oz Sahin,Edoardo Bertone,Abel Silva Vieira,Andrea Castelletti,Andrea Cominola,Matteo Giuliani,Damien Giurco,Michael Blumenstein,Andrea Turner,Ariane Liu,Steven Kenway,Dragan Savic,Christos Makropoulos,Panagiotis Kossieris +17 more
TL;DR: This paper provides a vision of the required transformative process and features of an integrated multi-utility service provider covering the system architecture, opportunities and benefits, impediments and strategies, and business opportunities.
Journal ArticleDOI
Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM)
TL;DR: In this paper, an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), was proposed for generating historical bridge condition ratings using limited bridge inspection records.
Journal ArticleDOI
Cloud computing as a facilitator of SME entrepreneurship
TL;DR: This research examines how Cloud technologies facilitate the development of internationally orientated small- and medium-sized enterprise (SME) entrepreneurship by providing greater access to global markets, lowering opportunity costs and supporting collaboration and innovation in an increasingly connected world.
Proceedings ArticleDOI
Performance of an Off-Line Signature Verification Method Based on Texture Features on a Large Indic-Script Signature Dataset
TL;DR: There were no remarkable changes in the results obtained applying the LBP and ULBP features for verification when the BHSig260 and GPDS-100 signature datasets were used for experimentation.
Journal ArticleDOI
An investigation of the modified direction feature for cursive character recognition
TL;DR: The modifieddirection feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours and is extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image.