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Introduction to Image Processing Using R [electronic resource] : Learning by Examples / by Alejandro C. Frery, Talita Perciano.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: London : Springer London : Imprint: Springer, 2013Description: XV, 87 p. 42 illus., 17 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781447149507
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.6 23
  • 006.37 23
LOC classification:
  • TA1637-1638
  • TA1634
Online resources:
Contents:
Definitions and Notation -- Image Data Formats and Color Representation -- Reading and Writing Images with R: Generating High Quality Output -- Contrast Manipulation -- Filters in the Image Domain -- Contrast Enhancement and Dimensionality Reduction by Principal.
In: Springer eBooksSummary: 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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Definitions and Notation -- Image Data Formats and Color Representation -- Reading and Writing Images with R: Generating High Quality Output -- Contrast Manipulation -- Filters in the Image Domain -- Contrast Enhancement and Dimensionality Reduction by Principal.

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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