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eBook Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images (Studies in Computational Intelligence) ePub

eBook Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images (Studies in Computational Intelligence) ePub

by Various,Valentina Zharkova

  • ISBN: 3642080006
  • Category: Computer Science
  • Subcategory: Computers
  • Author: Various,Valentina Zharkova
  • Language: English
  • Publisher: Springer; Softcover reprint of hardcover 1st ed. 2007 edition (November 25, 2010)
  • Pages: 392
  • ePub book: 1263 kb
  • Fb2 book: 1769 kb
  • Other: azw rtf doc docx
  • Rating: 4.6
  • Votes: 328

Description

V Feature Recognition Feature Recognition in Solar Images Image Enhancement Solar Feature Detection Statistica artificial.

Schetinin, Valentina Zharkova, A. Brazhnikov, S. I. Zharkov, Emanuele Salerno, Luigi Bedini et al. Pages 151-338. Feature Recognition and Classification Using Spectral Methods. This book presents innovative techniques in Recognition and Classification of Astrophysical and Medical Images. Feature Recognition Feature Recognition in Solar Images Image Enhancement Solar Feature Detection Statistica artificial intelligence classification cognition cybernetics information processing intelligence mathematics pattern pattern recognition.

This book presents innovative techniques in recognition and .

This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.

Introduction to pattern recognition and classification in astrophysical and medical images. This book can be used as a text book for students of Computing, Cybernetics, Applied Mathematics and Astrophysics

Introduction to pattern recognition and classification in astrophysical and medical images. Image standardization and enhancement. Region-based methods for pattern recognition in medical and astrophysical images. Advanced information processing using statistical methods. This book can be used as a text book for students of Computing, Cybernetics, Applied Mathematics and Astrophysics.

Автор: Valentina Zharkova Название: Artificial Intelligence in Recognition and Classification of. .

The first applications of pattern recognition techniques were for the analysis of medical X rays and MMR images that enabled the extraction of quantified information in terms of texture, intensity and shape and allowed to significantly improve a di agnosis of human organs.

The computer vision community has developed various image saliency detection algorithms, and we assessed which algorithm best.

The contents include:, Introduction to pattern recognition and classification in astrophysical and medical images Feature recognition and classification using spectral method The book is.The computer vision community has developed various image saliency detection algorithms, and we assessed which algorithm best matches the interpreters data observation patterns for magnetic target-spotting exercises.

Items related to Artificial Intelligence in Recognition and Classification. Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images (Studies in Computational Intelligence). ISBN 13: 9783540475118.

oceedings{tionTP, title {Introduction to Pattern Recognition and Classification .

oceedings{tionTP, title {Introduction to Pattern Recognition and Classification in Medical and Astrophysical Images}, author {Valentina V. Zharkova and Lakhmi C. Jain}, booktitle {Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images}, year {2007} }. Valentina V. Zharkova, Lakhmi C. Jain.

By using growing databases of medical images processed with pattern recognition and classification t echniques, one can produce fast and consistent diagnosis of diseases based on the accumulated knowledge obtained from many other similar cases from the stored.

By using growing databases of medical images processed with pattern recognition and classification t echniques, one can produce fast and consistent diagnosis of diseases based on the accumulated knowledge obtained from many other similar cases from the stored databases. The use of CCD cameras for astroph ysical instruments on the ground and space produce digitized images in va rious fields of astrophysics. In the past decade, many space and ground based instruments provide large numbers of digitized images of the ni ght skies and of the Sun, our closest star.

Studies in Computational Intelligence. English Valentina Zharkova is Chair of Applied Mathematics at the Department of Mathematics and Computing, University of Bradford, UK. Having obtained. Valentina Zharkova is Chair of Applied Mathematics at the Department of Mathematics and Computing, University of Bradford, UK. Having obtained her . c. degree from Taras Shevchenko National University of Kiev (NUK), Ukraine, and her P. degree from the Main Astronomical Observatory of the Ukrainian Academy of Sciences, she worked for NUK for 20 years.

Studies in Artificial Intelligence. as Want to Read: Want to Read savin. ant to Read. The contents include: introduction to pattern recognition and classification in astrophysical and medical images; image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information process This book presents innovative techniques in Recognition and Classification of Astrophysical and Medical Images.

This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.