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Texture Feature Extraction Techniques for Image Recognition / by Jyotismita Chaki, Nilanjan Dey.

By: Chaki, Jyotismita, autor
Contributor(s): SpringerLink (Online service) | Dey, Nilanjan, (1984-), autor.
Material type: materialTypeLabelE-bookSeries: (SpringerBriefs in Computational Intelligence, 2625-3704); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Singapore : Springer Singapore : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: 1 recurso en línea (XIV, 100 páginas) : 75 ilustraciones, 12 ilustraciones a color..ISBN: 9789811508530.Subject: FotónicaOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- Statistical Texture Features -- Structural Texture Features -- Signal Processed Texture Features -- Model Based Texture Features -- Applications of Texture Features.
In: Springer eBooksAbstract: The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TA1654 2020 EB (Browse shelf(Opens below)) Acceso electrónico eBook04032110
Total holds: 0

Introduction -- Statistical Texture Features -- Structural Texture Features -- Signal Processed Texture Features -- Model Based Texture Features -- Applications of Texture Features.

The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.

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