What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Michael Blumenstein mainly investigates Artificial intelligence, Feature extraction, Pattern recognition, Handwriting recognition and Artificial neural network.
His Artificial intelligence research includes elements of Signature, Machine learning and Computer vision.
His Feature extraction research integrates issues from Iris recognition, Biometrics, Contextual image classification, Sclera and Pattern recognition.
His biological study spans a wide range of topics, including Pixel and Feature.
As part of one scientific family, Michael Blumenstein deals mainly with the area of Handwriting recognition, narrowing it down to issues related to the Handwriting, and often Stroke order.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Structural reliability, Management system, Strategic planning, Traffic volume and Operations research.
His most cited work include:
- A novel feature extraction technique for the recognition of segmented handwritten characters (102 citations)
- Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011) (79 citations)
- A modified direction feature for cursive character recognition (76 citations)
What are the main themes of his work throughout his whole career to date?
Michael Blumenstein focuses on Artificial intelligence, Pattern recognition, Feature extraction, Artificial neural network and Computer vision.
Michael Blumenstein frequently studies issues relating to Machine learning and Artificial intelligence.
His studies in Pattern recognition integrate themes in fields like Signature, Feature, Biometrics, Pixel and Contextual image classification.
He has included themes like Classifier, Speech recognition, Convolutional neural network and Identification in his Feature extraction study.
His work in the fields of Backpropagation overlaps with other areas such as Liquefaction.
His Support vector machine research is multidisciplinary, relying on both Image processing and Word error rate.
He most often published in these fields:
- Artificial intelligence (61.66%)
- Pattern recognition (33.44%)
- Feature extraction (29.45%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (61.66%)
- Pattern recognition (33.44%)
- Cluster analysis (4.91%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Cluster analysis, Pixel and Deep learning.
Michael Blumenstein regularly links together related areas like Computer vision in his Artificial intelligence studies.
His work on Classification rate as part of general Pattern recognition study is frequently linked to Fourier transform, therefore connecting diverse disciplines of science.
In his study, which falls under the umbrella issue of Pixel, Component, Coherence and Frame is strongly linked to Process.
His research in Handwriting intersects with topics in Handwriting recognition and Natural language processing.
The Feature extraction study combines topics in areas such as Set and Support vector machine.
Between 2018 and 2021, his most popular works were:
- Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification (15 citations)
- Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection (14 citations)
- Drone-vs-Bird Detection Challenge at IEEE AVSS2019 (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Support vector machine, Feature extraction and Pixel.
The study incorporates disciplines such as Drone and Computer vision in addition to Artificial intelligence.
His Pattern recognition study which covers Benchmark that intersects with Noisy text and Feature.
The concepts of his Support vector machine study are interwoven with issues in Classifier, Statistical hypothesis testing, Elastic net regularization and Distance measures.
His study explores the link between Feature extraction and topics such as Set that cross with problems in Bengali, Handwriting, Identification and Natural language processing.
Michael Blumenstein usually deals with Pixel and limits it to topics linked to Feature vector and Sobel operator, Contextual image classification and k-nearest neighbors algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
