Verfügbarkeit: Sensor data understanding

Sensor data understanding:

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Grzegorzek, Marcin 1977- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Berlin Logos Verlag [2017]
Schlagworte:	Objektverfolgung Aktivität Capturing Szenenanalyse Information Retrieval Mustererkennung Objekterkennung Maschinelles Lernen Multimedia Sensorsystem Pattern Recognition Machine Learning Sensor Data Interpretation
Online-Zugang:	Inhaltsverzeichnis Inhaltstext Inhaltsverzeichnis
Beschreibung:	VI, 227 Seiten Illustrationen, Diagramme
ISBN:	9783832546335

Internformat

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Datensatz im Suchindex

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adam_text	CONTENTS PREFACE V I INTRODUCTION 1 1 FUNDAMENTAL CONCEPT 3 1.1 M O TIV A TIO N ............................................................................ 5 1.2 ACTIVE AND ASSISTED L IV IN G ................................................... 6 1.3 DIGITAL M E D ICIN E .................................................................. 9 1.4 OUTLINE AND CONTRIBUTION ................................................... 11 REFERENCES ..................................................................................... 13 II VISUAL SCENE ANALYSIS 17 2 LARGE-SCALE MULTIMEDIA RETRIEVAL 19 2.1 HIERARCHICAL ORGANISATION OF SEMANTIC MEANINGS............................................................................... 19 2.2 CONCEPT D ETECTION ............................................................... 24 2.2.1 GLOBAL VERSUS LOCAL FEATURES................................... 24 2.2.2 FEATURE LEARNING...................................................... 28 2.3 EVENT RETRIEVAL...................................................................... 31 2.3.1 EVENT RETRIEVAL WITHIN IM AGES/S HOTS ................... 32 2.3.2 EVENT RETRIEVAL OVER SHOT SEQUENCES ................... 33 2.4 CONCLUSION AND FUTURE T RE N D S ............................................ 34 2.4.1 R EASONING ............................................................... 35 2.4.2 UNCERTAINTIES IN CONCEPT D E TE C TIO N ...................... 35 2.4.3 ADAPTIVE LEARNING . .................................................. 36 REFERENCES...................................................................................... 39 3 SHAPE-BASED OBJECT RECOGNITION 53 3.1 PROBLEM STATEMENT AND M O TIV A TIO N ................................... 53 3.2 SHAPE REPRESENTATION ......................................................... 54 3.2.1 SURVEY OF RELATED M E TH O D S ................................... 54 3.2.2 COARSE-GRAINED SHAPE REPRESENTATION ................... 57 3.2.3 FINE-GRAINED SHAPE R EPRESENTATION ...................... 58 3.3 SHAPE M A TC H IN G ................................................................... 61 3.3.1 SURVEY OF RELATED M E TH O D S ................................... 61 3.3.2 SHAPE MATCHING USING COARSE-GRAINED FEATURES . . 63 3.3.3 SHAPE MATCHING USING FINE-GRAINED FEATURES . . . 64 3.4 EXPERIMENTS AND RESULTS ...................................................... 65 3.4.1 SHAPE RETRIEVAL USING COARSE-GRAINED FEATURES . . 65 3.4.2 SHAPE RETRIEVAL USING FINE-GRAINED FEATURES . . . 67 3.5 CONCLUSION AND FUTURE T RE N D S............................................. 68 REFERENCES...................................................................................... 71 4 MOVING OBJECT ANALYSIS FOR VIDEO INTERPRETATION 81 4.1 OBJECT TRACKING IN 2D VIDEO .............................................. 81 4.1.1 SURVEY OF RELATED APPROACHES........................... 82 4.1.2 TRACKING-LEARNING-DETECTION .............................. 89 4.1.3 TRACKING IN OMNIDIRECTIONAL V IDEO .................... 90 4.1.4 EXPERIMENTS AND RESULTS .................................... 92 4.2 3D TRAJECTORY EXTRACTION FROM 2D V ID E O .......................... 93 4.2.1 RJ-MCMC PARTICLE F ILTE RIN G .............................. 94 4.2.2 CONVOY DETECTION IN CROWDED VIDEO ............... 99 4.2.3 EXPERIMENTS AND RESULTS ...................................... 102 4.3 CONCLUSION AND FUTURE T RE N D S............................................. 104 REFERENCES...................................................................................... 106 III HUMAN DATA INTERPRETATION 111 5 PHYSICAL ACTIVITY RECOGNITION 113 5.1 ATOMIC ACTIVITY RECOGNITION ............................................ 113 5.1.1 SURVEY OF RELATED APPROACHES ............................. 115 5.1.2 CODEBOOK APPROACH FOR CLASSIFICATION ................ 118 5.1.3 EXPERIMENTS AND RESULTS ...................................... 124 5.2 GAIT RECOGNITION................................................................... 129 5.2.1 SURVEY OR RELATED APPROACHES .............................. 130 5.2.2 SPATIOTEMPORAL REPRESENTATION OF G A I T ............ 132 5.2.3 EXPERIMENTS AND RESULTS.................................... 135 5.3 CONCLUSION AND FUTURE T RE N D S ............................................. 141 REFERENCES ..................................................................................... 144 6 COGNITIVE ACTIVITY RECOGNITION 157 6.1 DEFINITION, TAXONOMY, IMPACT ON HEALTH ......................... 157 6.2 SENSING THE BRAIN A C TIV ITY ................................................... 158 6.2.1 ELECTROENCEPHALOGRAPHY.................................... 158 6.2.2 ELECTROOCULOGRAPHY ............................................. 159 6.2.3 FUNCTIONAL MAGNETIC RESONANCE IM A G IN G ......... 159 6.2.4 FUNCTIONAL NEAR-INFRARED SPECTROSCOPY.............159 6.3 SURVEY OF RELATED METHODS ............................................... 159 6.4 ELECTROOCULOGRAPHY-BASED APPROACH................................... 161 6.4.1 COGNITIVE ACTIVITY RECOGNITION M E TH O D .............161 6.4.2 INVESTIGATING CODEWORDS.................................... 162 6.5 APPLICATION AND VALIDATION................................................... 163 6.5.1 COLLECTING A D A TA SE T .......................................... 163 6.5.2 IMPLEMENTATION DETAILS ......................................... 165 6.5.3 RESULTS FOR COGNITIVE ACTIVITY RECOGNITION ......... 165 6.5.4 RESULTS FOR CODEWORDS INVESTIGATION ................... 166 6.6 CONCLUSION AND FUTURE T RE N D S ............................................ 167 REFERENCES..................................................................................... 169 7 EMOTION RECOGNITION 173 7.1 AUTOMATIC RECOGNITION OF EM OTIONS................................... 173 7.1.1 DEFINITION AND TAXONOMY OF EMOTIONS .................... 174 7.1.2 EXISTING TECHNIQUES FOR EMOTION RECOGNITION . . . 180 7.1.3 EMOTION RECOGNITION CHALLENGES ....................... 182 7.2 MULTIMODAL EMOTION R ECOGNITION ...................................... 186 7.2.1 AROUSAL/VALENCE E STIM ATION ............................. 186 7.2.2 BASIC EMOTION RECOGNITION ................................... 189 7.3 APPROACHES BASED ON PHYSIOLOGICAL D A T A ......................... 192 7.3.1 STRESS DETECTION USING HAND-CRAFTED FEATURES . . 194 7.3.2 CODEBOOK APPROACH FOR FEATURE GENERATION .... 196 7.3.3 DEEP NEURAL NETWORKS FOR FEATURE GENERATION . . 199 7.4 CONCLUSION AND FUTURE T RE N D S ................................................ 203 REFERENCES ......................................................................................... 204 IV CONCLUSION 211 8 SUMMARY AND FUTURE VISION 213 8.1 VISUAL SCENE ANALYSIS ............................................................. 213 8.2 HUMAN DATA INTERPRETATION ....................................................215 8.3 DATA-DRIVEN S O C IE TY ............................................................ 217 REFERENCES.......................................................................................... 219 LIST OF FIGURES 223 LIST OF TABLES 227
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publisher	Logos Verlag
record_format	marc
spelling	Grzegorzek, Marcin 1977- Verfasser (DE-588)133060578 aut Sensor data understanding Marcin Grzegorzek Berlin Logos Verlag [2017] VI, 227 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Objektverfolgung (DE-588)4311226-2 gnd rswk-swf Aktivität (DE-588)4141766-5 gnd rswk-swf Capturing (DE-588)4546178-8 gnd rswk-swf Szenenanalyse (DE-588)4435333-9 gnd rswk-swf Information Retrieval (DE-588)4072803-1 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Objekterkennung (DE-588)4314334-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Multimedia (DE-588)4192358-3 gnd rswk-swf Sensorsystem (DE-588)4307964-7 gnd rswk-swf Pattern Recognition Machine Learning Sensor Data Interpretation Sensorsystem (DE-588)4307964-7 s Multimedia (DE-588)4192358-3 s Information Retrieval (DE-588)4072803-1 s Szenenanalyse (DE-588)4435333-9 s Objekterkennung (DE-588)4314334-9 s Objektverfolgung (DE-588)4311226-2 s Aktivität (DE-588)4141766-5 s Capturing (DE-588)4546178-8 s Mustererkennung (DE-588)4040936-3 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Logos Verlag Berlin (DE-588)1065538812 pbl Erscheint auch als Online-Ausgabe 978-3-8325-9245-5 B:DE-101 application/pdf http://d-nb.info/1149066377/04 Inhaltsverzeichnis X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=0308374b27d64260b88f3ed26593dc72&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030221517&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Grzegorzek, Marcin 1977- Sensor data understanding Objektverfolgung (DE-588)4311226-2 gnd Aktivität (DE-588)4141766-5 gnd Capturing (DE-588)4546178-8 gnd Szenenanalyse (DE-588)4435333-9 gnd Information Retrieval (DE-588)4072803-1 gnd Mustererkennung (DE-588)4040936-3 gnd Objekterkennung (DE-588)4314334-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Multimedia (DE-588)4192358-3 gnd Sensorsystem (DE-588)4307964-7 gnd
subject_GND	(DE-588)4311226-2 (DE-588)4141766-5 (DE-588)4546178-8 (DE-588)4435333-9 (DE-588)4072803-1 (DE-588)4040936-3 (DE-588)4314334-9 (DE-588)4193754-5 (DE-588)4192358-3 (DE-588)4307964-7
title	Sensor data understanding
title_auth	Sensor data understanding
title_exact_search	Sensor data understanding
title_full	Sensor data understanding Marcin Grzegorzek
title_fullStr	Sensor data understanding Marcin Grzegorzek
title_full_unstemmed	Sensor data understanding Marcin Grzegorzek
title_short	Sensor data understanding
title_sort	sensor data understanding
topic	Objektverfolgung (DE-588)4311226-2 gnd Aktivität (DE-588)4141766-5 gnd Capturing (DE-588)4546178-8 gnd Szenenanalyse (DE-588)4435333-9 gnd Information Retrieval (DE-588)4072803-1 gnd Mustererkennung (DE-588)4040936-3 gnd Objekterkennung (DE-588)4314334-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Multimedia (DE-588)4192358-3 gnd Sensorsystem (DE-588)4307964-7 gnd
topic_facet	Objektverfolgung Aktivität Capturing Szenenanalyse Information Retrieval Mustererkennung Objekterkennung Maschinelles Lernen Multimedia Sensorsystem
url	http://d-nb.info/1149066377/04 http://deposit.dnb.de/cgi-bin/dokserv?id=0308374b27d64260b88f3ed26593dc72&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030221517&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT grzegorzekmarcin sensordataunderstanding AT logosverlagberlin sensordataunderstanding

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