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5 matches in All Departments
Background modeling and foreground detection are important steps in
video processing used to detect robustly moving objects in
challenging environments. This requires effective methods for
dealing with dynamic backgrounds and illumination changes as well
as algorithms that must meet real-time and low memory requirements.
Incorporating both established and new ideas, Background Modeling
and Foreground Detection for Video Surveillance provides a complete
overview of the concepts, algorithms, and applications related to
background modeling and foreground detection. Leaders in the field
address a wide range of challenges, including camera jitter and
background subtraction. The book presents the top methods and
algorithms for detecting moving objects in video surveillance. It
covers statistical models, clustering models, neural networks, and
fuzzy models. It also addresses sensors, hardware, and
implementation issues and discusses the resources and datasets
required for evaluating and comparing background subtraction
algorithms. The datasets and codes used in the text, along with
links to software demonstrations, are available on the book’s
website. A one-stop resource on up-to-date models, algorithms,
implementations, and benchmarking techniques, this book helps
researchers and industry developers understand how to apply
background models and foreground detection methods to video
surveillance and related areas, such as optical motion capture,
multimedia applications, teleconferencing, video editing, and
human–computer interfaces. It can also be used in graduate
courses on computer vision, image processing, real-time
architecture, machine learning, or data mining.
Background modeling and foreground detection are important steps in
video processing used to detect robustly moving objects in
challenging environments. This requires effective methods for
dealing with dynamic backgrounds and illumination changes as well
as algorithms that must meet real-time and low memory requirements.
Incorporating both established and new ideas, Background Modeling
and Foreground Detection for Video Surveillance provides a complete
overview of the concepts, algorithms, and applications related to
background modeling and foreground detection. Leaders in the field
address a wide range of challenges, including camera jitter and
background subtraction. The book presents the top methods and
algorithms for detecting moving objects in video surveillance. It
covers statistical models, clustering models, neural networks, and
fuzzy models. It also addresses sensors, hardware, and
implementation issues and discusses the resources and datasets
required for evaluating and comparing background subtraction
algorithms. The datasets and codes used in the text, along with
links to software demonstrations, are available on the book's
website. A one-stop resource on up-to-date models, algorithms,
implementations, and benchmarking techniques, this book helps
researchers and industry developers understand how to apply
background models and foreground detection methods to video
surveillance and related areas, such as optical motion capture,
multimedia applications, teleconferencing, video editing, and
human-computer interfaces. It can also be used in graduate courses
on computer vision, image processing, real-time architecture,
machine learning, or data mining.
Handbook of Robust Low-Rank and Sparse Matrix Decomposition:
Applications in Image and Video Processing shows you how robust
subspace learning and tracking by decomposition into low-rank and
sparse matrices provide a suitable framework for computer vision
applications. Incorporating both existing and new ideas, the book
conveniently gives you one-stop access to a number of different
decompositions, algorithms, implementations, and benchmarking
techniques. Divided into five parts, the book begins with an
overall introduction to robust principal component analysis (PCA)
via decomposition into low-rank and sparse matrices. The second
part addresses robust matrix factorization/completion problems
while the third part focuses on robust online subspace estimation,
learning, and tracking. Covering applications in image and video
processing, the fourth part discusses image analysis, image
denoising, motion saliency detection, video coding, key frame
extraction, and hyperspectral video processing. The final part
presents resources and applications in background/foreground
separation for video surveillance. With contributions from leading
teams around the world, this handbook provides a complete overview
of the concepts, theories, algorithms, and applications related to
robust low-rank and sparse matrix decompositions. It is designed
for researchers, developers, and graduate students in computer
vision, image and video processing, real-time architecture, machine
learning, and data mining.
Handbook of Robust Low-Rank and Sparse Matrix Decomposition:
Applications in Image and Video Processing shows you how robust
subspace learning and tracking by decomposition into low-rank and
sparse matrices provide a suitable framework for computer vision
applications. Incorporating both existing and new ideas, the book
conveniently gives you one-stop access to a number of different
decompositions, algorithms, implementations, and benchmarking
techniques. Divided into five parts, the book begins with an
overall introduction to robust principal component analysis (PCA)
via decomposition into low-rank and sparse matrices. The second
part addresses robust matrix factorization/completion problems
while the third part focuses on robust online subspace estimation,
learning, and tracking. Covering applications in image and video
processing, the fourth part discusses image analysis, image
denoising, motion saliency detection, video coding, key frame
extraction, and hyperspectral video processing. The final part
presents resources and applications in background/foreground
separation for video surveillance. With contributions from leading
teams around the world, this handbook provides a complete overview
of the concepts, theories, algorithms, and applications related to
robust low-rank and sparse matrix decompositions. It is designed
for researchers, developers, and graduate students in computer
vision, image and video processing, real-time architecture, machine
learning, and data mining.
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