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

Por: Colaborador(es): Tipo de material: TextoTextoSeries SpringerBriefs in Computer Science | SpringerBriefs in Computer ScienceEditor: London : Springer London : Imprint: Springer, 2013Descripción: XV, 87 p. 42 illus., 17 illus. in color. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9781447149507
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 006.6 23
  • 006.37 23
Clasificación LoC:
  • TA1637-1638
  • TA1637-1638
Recursos en línea:
Contenidos:
Springer eBooksResumen: 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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