Big Visual Data Analysis - Scene Classification and Geometric Labeling (Paperback, 1st ed. 2016)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs in Electrical and Computer Engineering
Release date:	February 2016
First published:	2016
Authors:	Chen Chen • Yuzhuo Ren • C.-C.Jay Kuo
Dimensions:	235 x 155 x 7mm (L x W x T)
Format:	Paperback
Pages:	122
Edition:	1st ed. 2016
ISBN-13:	978-981-10-0629-6
Categories:	Books > Science & Mathematics > Mathematics > Combinatorics & graph theory Books > Computing & IT > Applications of computing > Image processing > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	981-10-0629-6
Barcode:	9789811006296

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