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Big Visual Data Analysis : Scene Classification and Geometric Labeling / by Chen Chen, Yuzhuo Ren, C-C Jay Kuo

By: Chen, Chen
Contributor(s): Ren, Yuzhuo | Kuo, C.-C. Jay
Material type: materialTypeLabelE-bookSeries: Publisher: Singapore : Springer, 2016Edition: 1st ed.Description: 1 recurso en línea (X, 122 p.) 94 il., 12 il. col..ISBN: 9789811006319.Subject: Visión por ordenadorDDC classification: 621.382 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources Summary: This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TA1634 .C446 2016 EB (Browse shelf(Opens below)) .i11601851 Acceso electrónico eBOOK .i11601851
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This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.

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