Astronomical Image and Data Analysis

Starck, J.-L.; Murtagh, F.; Starck, Jean-Luc; Murtagh, Fionn

ISBN 10: 3540428852 / ISBN 13: 9783540428855

Published by Springer, New York, 2002

About this Item

EXCELLENT Unmarked PAGES And BINDING And COVER. Hardback. No dust Jacket, as issued. Physicist owner name on flyleaf. Approximately 6 X 9 ½. 289 pages.

Seller Inventory # 006007

Bibliographic Details

• Title: Astronomical Image and Data Analysis
• Publisher: Springer, New York
• Publication Date: 2002
• Language: English
• ISBN 10: 3540428852
• ISBN 13: 9783540428855
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Near Fine
• Seller catalogs:
  • Science, especially Physics snd Math.

About this title

Synopsis

The book addresses not only students and professional astronomers and astrophysicists, but also serious amateur astronomers and specialists in earth observation, medical imaging, and data mining.

From the Back Cover

With information and scale as central themes, this comprehensive survey explains how to handle real problems in astronomical data analysis using a modern arsenal of powerful techniques. It treats those innovative methods of image, signal, and data processing that are proving to be both effective and widely relevant. The authors are leaders in this rapidly developing field and draw upon decades of experience. They have been playing leading roles in international projects such as the Virtual Observatory and the Grid.
The book addresses not only students and professional astronomers and astrophysicists, but also serious amateur astronomers and specialists in earth observation, medical imaging, and data mining. The coverage includes chapters or appendices on: detection and filtering; image compression; multichannel, multiscale, and catalog data analytical methods; wavelets transforms, Picard iteration, and software tools.

This second edition of Starck and Murtagh's highly appreciated reference again deals with topics that are at or beyond the state of the art. It presents material which is more algorithmically oriented than most alternatives and broaches new areas like ridgelet and curvelet transforms. Throughout the book various additions and updates have been made.