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Signal Processing for Computer Vision is a unique and thorough
treatment of the signal processing aspects of filters and operators
for low-level computer vision. Computer vision has progressed
considerably over recent years. From methods only applicable to
simple images, it has developed to deal with increasingly complex
scenes, volumes and time sequences. A substantial part of this book
deals with the problem of designing models that can be used for
several purposes within computer vision. These partial models have
some general properties of invariance generation and generality in
model generation. Signal Processing for Computer Vision is the
first book to give a unified treatment of representation and
filtering of higher order data, such as vectors and tensors in
multidimensional space. Included is a systematic organisation for
the implementation of complex models in a hierarchical modular
structure and novel material on adaptive filtering using tensor
data representation. Signal Processing for Computer Vision is
intended for final year undergraduate and graduate students as well
as engineers and researchers in the field of computer vision and
image processing.
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