Collision Detection Professors in Global

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Sankaran Iyer
University of New South Wales Australia
object detection
pedestrian detection
anomaly detection
computer vision
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Du Huynh
University of Western Australia Australia
object detection
computer vision
pattern recognition
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Roberto Cipolla
University of Cambridge United Kingdom
object detection
computer vision
face recognition
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Maria Amer
Concordia University Canada
object detection
computer vision
object tracking
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Michaela Queitsch-maitland
University of Manchester United Kingdom
colliders
neutrino detection
large hadron collider
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Allison Sekuler
McMaster University Canada
object recognition
face recognition
motion perception
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Leman Akoglu
Carnegie Mellon University United States of America
anomaly detection
fraud detection
outlier detection
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Boubakeur Boufama
University of Windsor Canada
computer vision
pattern recognition
three-dimensional computer vision
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Henning Flaecher
University of Bristol United Kingdom
colliders
dark matter detection
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Xuelian Cheng
Monash University Australia
object detection
computer vision
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Bastian Leibe
RWTH Aachen University Germany
object detection
computer vision
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Sanja Fidler
University of Toronto Canada
object detection
computer vision
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J.c. Van Gemert
Delft University of Technology Netherlands
detection
pattern recognition
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Shirley Dyke
Purdue University United States of America
damage detection
machine vision
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Shirley Dyke
Purdue University United States of America
damage detection
machine vision
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Marios Savvides
Carnegie Mellon University United States of America
object detection
face detection
face recognition
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Marios Savvides
Carnegie Mellon University United States of America
object detection
face detection
face recognition
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Pierre Tardif
University of Sherbrooke Canada
intrusion detection
anomaly detection
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Guofei Gu
Texas A&M University United States of America
intrusion detection
anomaly detection
botnet detection
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Erza Aminanto
Monash University Australia
intrusion detection
anomaly detection
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Georgios Fellouris
University of Illinois United States of America
sequential detection
anomaly detection
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