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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Offline Cursive Character Recognition: A state of the art comparison
TL;DR: This paper revisits Camastra's SVM study in order to explore the effects of using an alternative modified direction feature (MDF) vector representation, and to compare the performance of a RBF-based approach against both SVM and HVQ.
Journal ArticleDOI
A geometric and fractional entropy-based method for family photo classification
Maryam Asadzadeh Kaljahi,Palaiahnakote Shivakumara,Tianping Hu,Hamid A. Jalab,Rabha W. Ibrahim,Michael Blumenstein,Tong Lu,Mohamad Nizam Ayub +7 more
TL;DR: Experimental results show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate.
Journal ArticleDOI
A clustering solution for analyzing residential water consumption patterns
Shamsur Rahim,Khoi Nguyen,Rodney Anthony Stewart,Tanvir Ahmed,Damien Giurco,Michael Blumenstein +5 more
TL;DR: In this paper, a clustering approach was used to reveal residential water consumption patterns within metered data, which can be used to determine the population adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic.
Proceedings ArticleDOI
Age Estimation using Disconnectedness Features in Handwriting
V. Basavaraja,Palaiahnakote Shivakumara,Devanur S. Guru,Umapada Pal,Tong Lu,Michael Blumenstein +5 more
TL;DR: Experimental results show that the proposed method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features is effective and outperforms the state-of-the-art methods.
Journal Article
Parametric study on the Prediction of Wave-induced Liquefaction using an Artificial Neural Network Model
TL;DR: Zhang et al. as mentioned in this paper used an artificial neural network (ANN) model to estimate the waveinduced liquefaction in terms of wave and seabed sediment conditions to get the most accurate results.