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Image processing is concerned with the analysis and manipulation of
images by computer. Providing a thorough treatment of image
processing with an emphasis on those aspects most used in computer
graphics, the authors concentrate on describing and analyzing the
underlying concepts rather than on presenting algorithms or
pseudocode. As befits a modern introduction to this topic, a good
balance is struck between discussing the underlying mathematics and
the main topics: signal processing, data discretization, the theory
of colour and different colour systems, operations in images,
dithering and half-toning, warping and morphing and image
processing. This second edition reflects recent trends in science
andtechnology that exploit image processing in computer graphics
and vision applications. Stochastic image models and statistical
methods for image processing are covered as are: A modern approach
and new developments in the area, Probability theory for image
processing, Applications in image analysis and computer vision.
This book introduces the statistical software R to the image
processing community in an intuitive and practical manner. R brings
interesting statistical and graphical tools which are important and
necessary for image processing techniques. Furthermore, it has been
proved in the literature that R is among the most reliable,
accurate and portable statistical software available. Both the
theory and practice of R code concepts and techniques are presented
and explained, and the reader is encouraged to try their own
implementation to develop faster, optimized programs. Those who are
new to the field of image processing and to R software will find
this work a useful introduction. By reading the book alongside an
active R session, the reader will experience an exciting journey of
learning and programming.
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