Machine Learning for Vision-Based Motion Analysis (Record no. 15041)

MARC details
000 -LEADER
fixed length control field 05957nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-0-85729-057-1
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20170628033415.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 101119s2011 xxk| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780857290571
-- 978-0-85729-057-1
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-0-85729-057-1
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1637-1638
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1637-1638
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYT
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQV
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM012000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM016000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.6
Edition number 23
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.37
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Wang, Liang.
Relator term editor.
245 10 - TITLE STATEMENT
Title Machine Learning for Vision-Based Motion Analysis
Medium [electronic resource] :
Remainder of title Theory and Techniques /
Statement of responsibility, etc edited by Liang Wang, Guoying Zhao, Li Cheng, Matti Pietikäinen.
264 #1 -
-- London :
-- Springer London :
-- Imprint: Springer,
-- 2011.
300 ## - PHYSICAL DESCRIPTION
Extent XIV, 372 p.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
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-- rdamedia
338 ## -
-- online resource
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347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement Advances in Pattern Recognition,
International Standard Serial Number 2191-6586
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Part I: Manifold Learning and Clustering/Segmentation -- Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis -- Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis -- Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models -- Part II: Tracking -- Mixed-state Markov Models in Image Motion Analysis -- Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate -- Discriminative Multiple Target Tracking -- A Framework of Wire Tracking in Image Guided Interventions -- Part III: Motion Analysis and Behavior Modeling -- An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos -- Video-based Human Motion Estimation by Part-whole Gait Manifold Learning -- Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes -- Learning Behavioral Patterns of Time Series for Video-surveillance -- Part IV: Gesture and Action Recognition -- Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs -- Learning Transferable Distance Functions for Human Action Recognition.
520 ## - SUMMARY, ETC.
Summary, etc Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: Provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms Examines algorithms for clustering and segmentation, and manifold learning for dynamical models Describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction Discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy Explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data Investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval. Dr. Liang Wang is a lecturer at the Department of Computer Science at the University of Bath, UK, and is also affiliated to the National Laboratory of Pattern Recognition in Beijing, China. Dr. Guoying Zhao is an adjunct professor at the Department of Electrical and Information Engineering at the University of Oulu, Finland. Dr. Li Cheng is a research scientist at the Agency for Science, Technology and Research (A*STAR), Singapore. Dr. Matti Pietikäinen is Professor of Information Technology at the Department of Electrical and Information Engineering at the University of Oulu, Finland.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer vision.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Image Processing and Computer Vision.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zhao, Guoying.
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Cheng, Li.
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Pietikäinen, Matti.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9780857290564
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Advances in Pattern Recognition,
-- 2191-6586
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/978-0-85729-057-1">http://dx.doi.org/10.1007/978-0-85729-057-1</a>
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Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Central Library Central Library 28/06/2017 Springer EBook   E-38220 28/06/2017 28/06/2017 E-Book

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