Anomaly Detection Professors in Global

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Leman Akoglu
Carnegie Mellon University United States of America
anomaly detection
outlier detection
pattern discovery
fraud detection
machine learning
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Sankaran Iyer
University of New South Wales Australia
anomaly detection
object detection
pedestrian detection
behavior analysis
computer vision
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Anindya Das
Umeå University Sweden
anomaly detection
machine learning
adaptive deviation learning
computer vision
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Du Huynh
University of Western Australia Australia
object detection
pattern recognition
machine learning
computer vision
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Maria Amer
Concordia University Canada
object detection
activity recognition
object tracking
computer vision
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Guofei Gu
Texas A&M University United States of America
anomaly detection
intrusion detection
botnet detection
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Paula Branco
University of Ottawa Canada
anomaly detection
fraud detection
machine learning
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Christian Maack
Aalborg University Denmark
anomaly detection
fault detection
machine learning
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Mingda Xu
Australian National University Australia
anomaly detection
machine learning
computer vision
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Kim Blackmore
Australian National University Australia
anomalous change detection
pattern recognition
machine learning
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Leman Akoglu
Carnegie Mellon University United States of America
anomaly mining
pattern discovery
machine learning
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Marios Savvides
Carnegie Mellon University United States of America
object detection
face detection
machine learning
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Guillermo Suarez De Tangil
King's College London United Kingdom
fraud detection
malware detection
machine learning
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Fons Van Der Sommen
Eindhoven University of Technology Belgium
computer-aided detection
machine learning
computer vision
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Bastian Leibe
RWTH Aachen University Germany
object detection
machine learning
computer vision
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Andrew Zisserman
University of Oxford United Kingdom
object recognition
machine learning
computer vision
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Michael Bronstein
Imperial College London United Kingdom
pattern recognition
machine learning
computer vision
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Kin-choong Yow
University of Regina Canada
pattern recognition
machine learning
computer vision
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Juan Niebles
Stanford University United States of America
pattern recognition
machine learning
computer vision
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Len Hamey
Macquarie University Australia
pattern recognition
machine learning
computer vision
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Jean-françois Lalonde
Laval University Canada
pattern recognition
machine learning
computer vision
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