Holdings: Object detection using feature mining in a distributed machine learning framework

Object detection using feature mining in a distributed machine learning framework:

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Bibliographic Details
Main Author:	Ehlers, Arne (Author)
Format:	Thesis Book
Language:	English
Published:	Düsseldorf VDI Verlag GmbH [2017]
Edition:	Als Manuskript gedruckt
Series:	Fortschritt-Berichte VDI. Reihe 10, Informatik/Kommunikation Nr. 855
Subjects:	Boosting Maschinelles Lernen Dempster-Shafer-Theorie Merkmalsextraktion Objekterkennung Klassifikator > Informatik Hochschulschrift
Online Access:	Inhaltsverzeichnis
Physical Description:	XIII, 148 Seiten Illustrationen, Diagramme
ISBN:	9783183855100

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MARC


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Record in the Search Index

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adam_text	C ONTENTS A B B REVIATION S V II S YM B OLS AND N O TA TIO N IX A B STR A CT/K U RZFA SSU N G X II 1 IN TROD U CTION 1 2 R ELA TED W ORK AND D A TA S ETS 16 2.1 MACHINE LEARNING FOR VISUAL OBJECT D ETECTIO N ....................................... 17 2.1.1 FEATURE P ROVISION.......................................................................... 17 2.1.2 LEARNING A LG O RITH M S.................................................................... 19 2.2 DATA SETS AND BENCHMARKS....................................................................... 21 3 F UN DAM ENTALS 28 3.1 COMMON FEATURES...................................................................................... 29 3.1.1 HAAR-LIKE FEATURES ....................................................................... 29 3.1.2 HISTOGRAMS OF ORIENTED G RAD IEN TS .............................................. 31 3.1.3 FROM FEATURES TO C LASSIFIERS ....................................................... 33 3.2 SUPERVISED MACHINE L E A R N IN G ................................................................. 34 3.2.1 ADAPTIVE B O O S TIN G ....................................................................... 35 3.2.2 VIOLA AND JONES DETECTION FRAMEWORK ..................................... 38 3.2.3 MARGIN ANALYSIS............................................................................. 50 3.2.4 VARIANTS OF BOOSTING ALGORITHM S ................................................. 51 3.3 DATA A NALY SIS............................................................................................. 52 3.3.1 CLUSTER ANALYSIS............................................................................. 53 3.3.2 PRINCIPAL COMPONENT A NALYSIS .................................................... 56 3.4 DETECTOR PERFORMANCE M EASU RES.............................................................. 58 4 D ISTRIB U TED M ACHINE LEARNING FRAM EW ORK 62 5 LEARNING FROM SPARSE T RAINING D A TA 68 5.1 TRAINING DATA AUGMENTATION.................................................................... 70 5.2 EXPERIMENTAL R E SU LTS................................................................................ 71 5.2.1 EXPERIMENTS ON FACE D E TE C TIO N ................................................. 71 5.2.2 EXPERIMENTS ON CELL DATA S E T .................................................... 74 CONTENTS 5.3 D ISCUSSION.................................................................................................. 74 6 FRACTAL IN TEGRAL P A TH S 76 6.1 BOOSTED FRACTAL INTEGRAL P A TH S ................................................................. 79 6.1.1 F R A C TA LS .......................................................................................... 79 6.1.2 FRACTAL F EATURES.............................................................................. 80 6.1.3 FRACTAL PROPERTIES .......................................................................... 80 6.1.4 CONSTRUCTION OF F R A C TA LS .............................................................. 82 6.1.5 FEATURE T Y P E S................................................................................. 82 6.2 EXPERIMENTAL R E SU LTS................................................................................. 83 6.2.1 FACE DETECTION ............................................................................. 83 6.2.2 MICROSCOPIC CELL D ETECTION........................................................... 87 6.2.3 TRAINING AND COMPUTING T IM E..................................................... 89 6.3 D ISCUSSION................................................................................................... 89 7 M U LTI-F EATU RE M ININ G FOR D E TE C TO R L EARNING 90 7.1 2REC F E A TU RE S............................................................................................. 93 7.2 KEYPOINT HOG F E A TU R E S .......................................................................... 95 7.3 EXPERIMENTAL R E SU LTS................................................................................. 99 7.3.1 FACE DETECTION ............................................................................. 99 7.3.2 LATERAL CAR DETECTION ....................................................................110 7.3.3 PEDESTRIAN D E TE C TIO N .......................................................................113 7.3.4 INSIGHTS INTO THE TRAINING P RO C E SS ................................................. 118 7.4 D ISCUSSION......................................................................................................120 8 N ON -M AXIM U M S UP PRESSION U SING D E M P STE R S T H EO RY O F E V ID EN CE 121 8.1 MERGING MULTIPLE DETECTIONS BASED ON DEMPSTERS THEORY ....................123 8.1.1 CASCADED C LASSIFIER..........................................................................123 8.1.2 DEMPSTER-SHAFER THEORY OF E V ID E N C E ...........................................124 8.1.3 JOINT CONFIDENCE BASED ON D EM PSTER-S HAFER .............................. 125 8.1.4 CONFIDENCE-BASED DETECTION M E RG IN G ...........................................125 8.2 EXPERIMENTAL R E SU LTS................................................................................... 126 8.2.1 FACE DETECTION ................................................................................ 127 8.2.2 LATERAL CAR DETECTION ....................................................................129 8.3 D ISCUSSION......................................................................................................129 9 C ON CLU SION 131 A L INDENM AYER S Y STEM S D EFIN IN G FRACTAL IN TEGRAL P A TH S 135 A.L L-SYSTEM DEFINING GOSPER C U R V E .............................................................136 A.2 L-SYSTEM DEFINING E-CURVE .......................................................................136 B IB LIOGRAP H Y 138
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spelling	Ehlers, Arne Verfasser aut Object detection using feature mining in a distributed machine learning framework Dipl.-Ing. Arne Ehler, Hannover ; tnt Institut für Informationsverarbeitung Als Manuskript gedruckt Düsseldorf VDI Verlag GmbH [2017] © 2017 XIII, 148 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Fortschritt-Berichte VDI. Reihe 10, Informatik/Kommunikation Nr. 855 Dissertation Gottfried Wilhelm Leibniz Universität Hannover 2017 Boosting (DE-588)4839853-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Dempster-Shafer-Theorie (DE-588)4395834-5 gnd rswk-swf Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Objekterkennung (DE-588)4314334-9 gnd rswk-swf Klassifikator Informatik (DE-588)4288547-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Objekterkennung (DE-588)4314334-9 s Merkmalsextraktion (DE-588)4314440-8 s Maschinelles Lernen (DE-588)4193754-5 s Klassifikator Informatik (DE-588)4288547-4 s Boosting (DE-588)4839853-6 s Dempster-Shafer-Theorie (DE-588)4395834-5 s DE-604 Leibniz Universität Hannover Institut für Theoretische Nachrichtentechnik und Informationsverarbeitung Sonstige (DE-588)5123663-1 oth Fortschritt-Berichte VDI. Reihe 10, Informatik/Kommunikation Nr. 855 (DE-604)BV000897204 855 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029950001&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Ehlers, Arne Object detection using feature mining in a distributed machine learning framework Fortschritt-Berichte VDI. Reihe 10, Informatik/Kommunikation Boosting (DE-588)4839853-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Dempster-Shafer-Theorie (DE-588)4395834-5 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Objekterkennung (DE-588)4314334-9 gnd Klassifikator Informatik (DE-588)4288547-4 gnd
subject_GND	(DE-588)4839853-6 (DE-588)4193754-5 (DE-588)4395834-5 (DE-588)4314440-8 (DE-588)4314334-9 (DE-588)4288547-4 (DE-588)4113937-9
title	Object detection using feature mining in a distributed machine learning framework
title_auth	Object detection using feature mining in a distributed machine learning framework
title_exact_search	Object detection using feature mining in a distributed machine learning framework
title_full	Object detection using feature mining in a distributed machine learning framework Dipl.-Ing. Arne Ehler, Hannover ; tnt Institut für Informationsverarbeitung
title_fullStr	Object detection using feature mining in a distributed machine learning framework Dipl.-Ing. Arne Ehler, Hannover ; tnt Institut für Informationsverarbeitung
title_full_unstemmed	Object detection using feature mining in a distributed machine learning framework Dipl.-Ing. Arne Ehler, Hannover ; tnt Institut für Informationsverarbeitung
title_short	Object detection using feature mining in a distributed machine learning framework
title_sort	object detection using feature mining in a distributed machine learning framework
topic	Boosting (DE-588)4839853-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Dempster-Shafer-Theorie (DE-588)4395834-5 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Objekterkennung (DE-588)4314334-9 gnd Klassifikator Informatik (DE-588)4288547-4 gnd
topic_facet	Boosting Maschinelles Lernen Dempster-Shafer-Theorie Merkmalsextraktion Objekterkennung Klassifikator Informatik Hochschulschrift
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029950001&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV000897204
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