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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Journal ArticleDOI
Rotation invariant angle-density based features for an ice image classification system
Yue Shengkai,Minglei Yuan,Tong Lu,Palaiahnakote Shivakumara,Michael Blumenstein,Jie Shi,G. Hemantha Kumar +6 more
TL;DR: A new method uses wavelet decomposition to extract robust features, such as those invariant to rotation, scaling and thickness of ice for classification, and is useful for accurate ice detection with better accuracy.
Proceedings ArticleDOI
Marginal Noise Reduction in Historical Handwritten Documents -- A Survey
TL;DR: This survey helps researchers to identify appropriate methods according to the associated marginal noise and also illustrates their drawbacks in order to make suggestions for developing approaches, which are more general and robust for any datasets.
Journal ArticleDOI
Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach.
TL;DR: In this paper, a lattice structure combining kmers, (k - 1)mers and (k + 1)mermers was proposed for correcting indel errors in miRNA sequencing data.
Proceedings ArticleDOI
Fast local binary pattern: Application to document image retrieval
TL;DR: A non-parametric texture feature extraction method based on summarising the local grey-level structure of the image that provided promising results with lower computing time as well as smaller memory space consumption compared to other variation of LBP methods.
Journal ArticleDOI
A new augmentation-based method for text detection in night and day license plate images
TL;DR: This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images that uses a recognition concept to fix the bounding boxes, merging the bounded boxes and eliminating false positives, resulting in text/license plate detection in both day and night images.