Autor/Autorin: Hua Gang
Dr. Gang Hua is a Scientist in computer vision, pattern nrecognition, and machine learning in Microsoft Live labs nResearch. He obtained his Ph.D. degree in Electrical and nComputer Engineering from Northwestern University in June 2006. nHe has authored more than 30 peer reviewed papers. He also holds n1 US patent and has 15+ patents pending.
The explosive growth of the internet have accumulated gigantic amount of visual data. Management and retrieval of relevant information from it become a more and more difficult task. Moreover, as security becomes an important issue, there are numerous surveillance cameras all around the world which accumulate terabytes of video data every day. How to intelligently analyze these video data becomes a big challenge. These largely explain the boom of the research on complex motion analysis, the understanding of which would be essential for higher level tasks such as visual recognition and content understanding. This book provides a general framework on complex motion analysis based on probabilistic variational method. It provides a distributed computational paradigm, since each motion component is analyzed individually, with the constraints from the other components being reinforced. It is also a collaborative approach, since it naturally integrates multiple information cues to obtain the optimal solution.
The explosive growth of the internet have
accumulated gigantic amount of visual data.
Management and retrieval of relevant information
from it become a more and more difficult task.
Moreover, as security becomes an important issue,
there are numerous surveillance cameras all around
the world which accumulate terabytes of video data
every day. How to intelligently analyze these video
data becomes a big challenge. These largely explain
the boom of the research on complex motion analysis,
the understanding of which would be essential for
higher level tasks such as visual recognition and
content understanding. This book provides a general
framework on complex motion analysis based on
probabilistic variational method. It provides a
distributed computational paradigm, since each
motion component is analyzed individually, with the
constraints from the other components being
reinforced. It is also a collaborative approach,
since it naturally integrates multiple information
cues to obtain the optimal solution.
Dr. Gang Hua is a Scientist in computer vision, pattern
recognition, and machine learning in Microsoft Live labs
Research. He obtained his Ph.D. degree in Electrical and
Computer Engineering from Northwestern University in June 2006.
He has authored more than 30 peer reviewed papers. He also holds
1 US patent and has 15+ patents pending.
Über den Autor
Dr. Gang Hua is a Scientist in computer vision, pattern
recognition, and machine learning in Microsoft Live labs
Research. He obtained his Ph.D. degree in Electrical and
Computer Engineering from Northwestern University in June 2006.
He has authored more than 30 peer reviewed papers. He also holds
1 US patent and has 15+ patents pending.
Klappentext
The explosive growth of the internet have
accumulated gigantic amount of visual data.
Management and retrieval of relevant information
from it become a more and more difficult task.
Moreover, as security becomes an important issue,
there are numerous surveillance cameras all around
the world which accumulate terabytes of video data
every day. How to intelligently analyze these video
data becomes a big challenge. These largely explain
the boom of the research on complex motion analysis,
the understanding of which would be essential for
higher level tasks such as visual recognition and
content understanding. This book provides a general
framework on complex motion analysis based on
probabilistic variational method. It provides a
distributed computational paradigm, since each
motion component is analyzed individually, with the
constraints from the other components being
reinforced. It is also a collaborative approach,
since it naturally integrates multiple information
cues to obtain the optimal solution.
