MARC details
| 000 -LIDER |
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| fixed length control field |
04712nam a22006375i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
978-3-319-75508-3 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20210201191518.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180224s2018 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319755083 |
| -- |
978-3-319-75508-3 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TK5102.9 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA1637-1638 |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
TTBM |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
TEC008000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
TTBM |
| Source |
thema |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYS |
| Source |
thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
621.382 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Isupova, Olga. |
| Relator term |
author. |
| Relator code |
aut |
| -- |
http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT |
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| Title |
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc. |
by Olga Isupova. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. 2018. |
| 264 #1 - |
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| -- |
Cham : |
| -- |
Springer International Publishing : |
| -- |
Imprint: Springer, |
| -- |
2018. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XXV, 126 p. 27 illus., 25 illus. in color. |
| Other physical details |
online resource. |
| 336 ## - |
|---|
| -- |
text |
| -- |
txt |
| -- |
rdacontent |
| 337 ## - |
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| -- |
computer |
| -- |
c |
| -- |
rdamedia |
| 338 ## - |
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| -- |
online resource |
| -- |
cr |
| -- |
rdacarrier |
| 347 ## - |
|---|
| -- |
text file |
| -- |
PDF |
| -- |
rda |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Springer Theses, Recognizing Outstanding Ph.D. Research, |
| International Standard Serial Number |
2190-5053 |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Acceso multiusuario |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction -- Background -- Proposed Learning Algorithms for Markov Clustering Topic Model -- Dynamic Hierarchical Dirlchlet Process -- Change Point Detection with Gaussian Processes -- Conclusions and Future Work. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes. Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed. The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers. |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Owner |
UABC ; |
| Method of acquisition |
Temporal ; |
| Date of acquisition |
01/01/2021-12/31/2023. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Signal processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Image processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Speech processing systems. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Optical data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Signal, Image and Speech Processing. |
| -- |
https://scigraph.springernature.com/ontologies/product-market-codes/T24051 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Image Processing and Computer Vision. |
| -- |
https://scigraph.springernature.com/ontologies/product-market-codes/I22021 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Artificial Intelligence. |
| -- |
https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Computational Intelligence. |
| -- |
https://scigraph.springernature.com/ontologies/product-market-codes/T11014 |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783319755076 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783319755090 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783030092504 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Springer Theses, Recognizing Outstanding Ph.D. Research, |
| -- |
2190-5053 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Public note |
Libro electrónico |
| Uniform Resource Identifier |
http://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-75508-3 |
| 912 ## - |
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| -- |
ZDB-2-ENG |
| 912 ## - |
|---|
| -- |
ZDB-2-SXE |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Libro Electrónico |
