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Emerging trends in image processing, computer vision and pattern recognition / [edited by] Leonidas Deligiannidis and Hamid R. Arabnia.

Contributor(s): Material type: TextTextSeries: Emerging trends in computer science & applied computingPublication details: Amsterdam : Elsevier, c2015.Description: xxxv, 609 p. : illustrations ; 24 cmISBN:
  • 9780128020456
Subject(s): DDC classification:
  • 006.42 23 D353
Contents:
pt. 1. Image and signal processing -- pt. 2. Computer vision and recognition systems -- pt. 3. Registration, matching, and pattern recognition -- Index.
Summary: This book discusses the latest trends in imaging science, which at its core, consists of three intertwined computer science fields: image processing, computer vision, and pattern recognition. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new applications, editors, Deligiannidis and Arabnia cover: perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation; application techniques in computer vision; pattern recognition algorithms including, but not limited to; supervised and unsupervised classification algorithms, ensemble learning algorithms, and parsing algorithms; how to use image processing and visualization to analyze big data.
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Includes bibliographical references and index.

pt. 1. Image and signal processing --
pt. 2. Computer vision and recognition systems --
pt. 3. Registration, matching, and pattern recognition --
Index.

This book discusses the latest trends in imaging science, which at its core, consists of three intertwined computer science fields: image processing, computer vision, and pattern recognition. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new applications, editors, Deligiannidis and Arabnia cover: perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation; application techniques in computer vision; pattern recognition algorithms including, but not limited to; supervised and unsupervised classification algorithms, ensemble learning algorithms, and parsing algorithms; how to use image processing and visualization to analyze big data.

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