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
More filters
Journal ArticleDOI
A novel approach for structural feature extraction: contour vs. direction
TL;DR: The paper presents a novel approach for extracting structural features from segmented cursive handwriting based on the contour code and stroke direction, and the experimental results are very promising.
Journal ArticleDOI
A rapid analytical method for predicting the oxygen demand of wastewater
TL;DR: It was found that the most accurate results were obtained when a spectral range of 190–350 nm was provided as data input to the ANN, and when using unfiltered samples below a turbidity range of 150 NTU, indicating that samples can be measured directly without the additional need for preprocessing by filtering.
Book ChapterDOI
Automatic Bridge Crack Detection A Texture Analysis-Based Approach
TL;DR: An automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images using a sliding window-based technique is proposed.
Proceedings ArticleDOI
Texture-based feature mining for crowd density estimation: A study
TL;DR: This paper comprehensively reviewed different texture features and their different possible combinations to evaluate their performance on pedestrian crowds and proposed two-stage classification and regression based framework for performance evaluation.
Journal ArticleDOI
Prediction of Long-Term Bridge Performance: Integrated Deterioration Approach with Case Studies
TL;DR: In this article, an advanced integrated bridge deterioration approach has been developed that incorporates a time-based model, a state-based state model with the Elman neural network (ENN) and a backward prediction model (BPM) to predict long-term bridge performance.