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Handbook of Robust Low-Rank and Sparse Matrix Decomposition - Applications in Image and Video Processing (Hardcover)
Loot Price: R4,915
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Handbook of Robust Low-Rank and Sparse Matrix Decomposition - Applications in Image and Video Processing (Hardcover)
Expected to ship within 12 - 17 working days
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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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