Holdings: Machine vision algorithms and applications

Machine vision algorithms and applications:

Saved in:

Bibliographic Details
Main Authors:	Steger, Carsten (Author), Ulrich, Markus (Author), Wiedemann, Christian (Author)
Format:	Book
Language:	English
Published:	Weinheim Wiley-VCH [2018]
Edition:	2nd, completely revised and enlarged edition
Subjects:	Maschinelles Sehen Mustererkennung Algorithmen u. Datenstrukturen Algorithms & Data Structures Bildgebende Systeme u. Verfahren Bildgebendes Verfahren Computer Science Electrical & Electronics Engineering Elektrotechnik u. Elektronik Imaging Systems & Technology Informatik Optics & Photonics Optik u. Photonik Physics Physik
Online Access:	http://www.wiley-vch.de/publish/dt/books/ISBN978-3-527-41365-2/ Inhaltsverzeichnis
Physical Description:	xxii, 494 Seiten Illustrationen, Diagramme
ISBN:	9783527413652

Staff View

MARC


LEADER	00000nam a22000008c 4500
001	BV044670270
003	DE-604
005	20200701
007	t
008	171206s2018 gw a\|\|\| \|\|\|\| 00\|\|\| eng d
015			\|a 17,N41 \|2 dnb
016	7		\|a 1140659693 \|2 DE-101
020			\|a 9783527413652 \|c pbk \|9 978-3-527-41365-2
024	3		\|a 9783527413652
028	5	2	\|a Bestellnummer: 1141365 000
035			\|a (OCoLC)1022089388
035			\|a (DE-599)DNB1140659693
040			\|a DE-604 \|b ger \|e rda
041	0		\|a eng
044			\|a gw \|c XA-DE-BW
049			\|a DE-M347 \|a DE-1050 \|a DE-11 \|a DE-355 \|a DE-29T \|a DE-634 \|a DE-91G \|a DE-573 \|a DE-703 \|a DE-898 \|a DE-861
082	0		\|a 530 \|2 23
084			\|a ST 330 \|0 (DE-625)143663: \|2 rvk
084			\|a DAT 760f \|2 stub
084			\|a 530 \|2 sdnb
100	1		\|a Steger, Carsten \|e Verfasser \|4 aut
245	1	0	\|a Machine vision algorithms and applications \|c Carsten Steger, Markus Ulrich, and Christian Wiedemann
250			\|a 2nd, completely revised and enlarged edition
264		1	\|a Weinheim \|b Wiley-VCH \|c [2018]
300			\|a xxii, 494 Seiten \|b Illustrationen, Diagramme
336			\|b txt \|2 rdacontent
337			\|b n \|2 rdamedia
338			\|b nc \|2 rdacarrier
650	0	7	\|a Maschinelles Sehen \|0 (DE-588)4129594-8 \|2 gnd \|9 rswk-swf
650	0	7	\|a Mustererkennung \|0 (DE-588)4040936-3 \|2 gnd \|9 rswk-swf
653			\|a Algorithmen u. Datenstrukturen
653			\|a Algorithms & Data Structures
653			\|a Bildgebende Systeme u. Verfahren
653			\|a Bildgebendes Verfahren
653			\|a Computer Science
653			\|a Electrical & Electronics Engineering
653			\|a Elektrotechnik u. Elektronik
653			\|a Imaging Systems & Technology
653			\|a Informatik
653			\|a Maschinelles Sehen
653			\|a Mustererkennung
653			\|a Optics & Photonics
653			\|a Optik u. Photonik
653			\|a Physics
653			\|a Physik
689	0	0	\|a Maschinelles Sehen \|0 (DE-588)4129594-8 \|D s
689	0	1	\|a Mustererkennung \|0 (DE-588)4040936-3 \|D s
689	0		\|5 DE-604
700	1		\|a Ulrich, Markus \|e Verfasser \|4 aut
700	1		\|a Wiedemann, Christian \|e Verfasser \|4 aut
710	2		\|a Wiley-VCH \|0 (DE-588)16179388-5 \|4 pbl
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe, PDF \|z 978-3-527-81290-5
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe, EPUB \|z 978-3-527-81289-9
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe, MOBI \|z 978-3-527-81291-2
780	0	0	\|i Vorangegangen ist \|z 9783527407347
856	4	2	\|m X:MVB \|u http://www.wiley-vch.de/publish/dt/books/ISBN978-3-527-41365-2/
856	4	2	\|m DNB Datenaustausch \|q application/pdf \|u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030067628&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA \|3 Inhaltsverzeichnis
999			\|a oai:aleph.bib-bvb.de:BVB01-030067628

Record in the Search Index

_version_	1804178116170481664
adam_text	CONTENTS LIST OF ABBREVIATIONS XV PREFACE TO THE SECOND EDITION XIX PREFACE TO THE FIRST EDITION XXI 1 INTRODUCTION 1 2 IMAGE ACQUISITION 5 2.1 ILLUM INATION........................................................................................... 5 2.1.1 ELECTROMAGNETIC R A D IA TIO N ..................................................... 5 2.1.2 TYPES OF LIGHT S O U R C E S ............................................................ 7 2.1.3 INTERACTION OF LIGHT AND M A TTE R ............................................... 8 2.1.4 USING THE SPECTRAL COMPOSITION OF THE ILLU M IN ATIO N .............. 10 2.1.5 USING THE DIRECTIONAL PROPERTIES OF THE ILLUM INATION............. 12 2.1.5.1 DIFFUSE BRIGHT-FIELD FRONT LIGHT ILLUMINATION . . . 13 2.1.5.2 DIRECTED BRIGHT-FIELD FRONT LIGHT ILLUMINATION . . . 14 2.1.5.3 DIRECTED DARK-FIELD FRONT LIGHT ILLUMINATION . . . 15 2.1.5.4 DIFFUSE BRIGHT-FIELD BACK LIGHT ILLUMINATION . . . 16 2.1.5.5 TELECENTRIC BRIGHT-FIELD BACK LIGHT ILLUMINATION . 17 2.2 L E N S E S ..................................................................................................... 18 2.2.1 PINHOLE CAMERAS ..................................................................... 18 2.2.2 GAUSSIAN O P TIC S ........................................................................ 19 2.2.2.1 R EFRACTIO N .................................................................. 19 2 2 2 .2 THICK LENS M O D E L ..................................................... 20 2.2.2.3 APERTURE STOP AND P U P I L S ......................................... 22 2.2.2.4 RELATION OF THE PINHOLE MODEL TO GAUSSIAN OPTICS . 24 2.2.3 DEPTH OF F IE LD ........................................................................... 25 2.2.4 TELECENTRIC L ENSES..................................................................... 28 2.2.4.1 OBJECT-SIDE TELECENTRIC L E N SE S ............................... 28 2.2.4.2 BILATERAL TELECENTRIC L ENSES...................................... 30 2.2.4.3 IMAGE-SIDE TELECENTRIC L E N S E S ............................... 31 2.2.4.4 PROJECTION CHARACTERISTICS OF L E N S E S ...................... 32 2.2.5 TILT LENSES AND THE SCHEIMPFLUG P RIN C IP LE ............................ 32 2.2.6 LENS A B E RRA TIO N S ...................................................................... 36 2.2.6.1 SPHERICAL A B E RRA TIO N ................................................ 36 2.2.6.2 C O M A ......................................................................... 36 2.2.6.3 A STIGM ATISM ............................................................... 37 2.2.6.4 CURVATURE OF F IE LD ...................................................... 37 2.2.6.5 D ISTO RTIO N .................................................................. 37 2.2.6.6 CHROMATIC A BERRATION ............................................... 39 22.6.1 EDGE-SPREAD FUNCTION ............................................... 39 2.2.6.S V IGNETTING................................................................... 40 2.3 C A M E RA S.................................................................................................. 41 2.3.1 CCD S E N S O R S ............................................................................ 41 2.3.1.1 LINE SENSORS................................................................ 41 2.3.1.2 FULL FRAME ARRAY S EN SO RS.......................................... 43 2.3.1.3 FRAME TRANSFER S E N SO RS............................................. 43 2.3.1.4 INTERLINE TRANSFER S EN SO RS.......................................... 44 2.3.1.5 READOUT M O D E S ......................................................... 46 2.3.2 CMOS S E N S O R S ......................................................................... 46 2.3.2.1 SENSOR A RCHITECTURE ................................................... 46 2.3.2.2 ROLLING AND GLOBAL SHUTTERS ...................................... 47 2.3.3 COLOR CAM ERAS............................................................................ 48 2.3.3.1 SPECTRAL RESPONSE OF MONOCHROME CAMERAS .... 48 2.3.3.2 SINGLE-CHIP C A M E RA S ................................................ 49 2.3.3.3 THREE-CHIP CAMERAS ................................................ 50 2.3.3.4 SPECTRAL RESPONSE OF COLOR C A M E RA S ....................... 50 2.3.4 SENSOR S IZ E S ............................................................................... 50 2.3.5 CAMERA P E RFO RM A N C E ............................................................... 52 2.3.5.1 NOISE ......................................................................... 52 2.3.5.2 SIGNAL-TO-NOISE R A TIO ............................................... 53 2.3.5.3 DYNAMIC R A N G E ......................................................... 54 2.3.5.4 NONUNIFORM ITIES......................................................... 54 2.4 CAMERA-COMPUTER INTERFACES............................................................... 55 2.4.1 ANALOG VIDEO S IG N A LS ................................................................ 56 2.4.1.1 ANALOG VIDEO STANDARDS .......................................... 56 2.4.1.2 ANALOG FRAME G RABBERS............................................. 58 2.4.2 DIGITAL VIDEO S IG N A LS ............................................................... 60 2.4.2.1 CAMERA L IN K ................................................................ 61 2.4.2.2 CAMERA LINK H S ......................................................... 62 2.4.2.3 COAX PRESS .................................................................. 64 2.4.2.4 IEEE 1394 .................................................................. 65 2.4.2.5 USB 2 . 0 ..................................................................... 67 2A 2.6 USB3 VISION ............................................................. 68 2A.2.1 GIGE V IS IO N ................................................................ 69 2.4.3 GENERIC INTERFACES ..................................................................... 72 2.4.3.1 G E N I C A M ................................................................... 72 2.4.3.2 GENICAM G E N T L ..................................................... 77 2.4.4 IMAGE ACQUISITION M O D E S.......................................................... 79 2.5 3D IMAGE ACQUISITION D E V IC E S ............................................................ 82 2.5.1 STEREO SENSORS............................................................................ 82 2.5.2 SHEET OF LIGHT SENSORS ............................................................ 84 2.5.3 STRUCTURED LIGHT S E N S O R S ......................................................... 86 2.5.3.1 PATTERN P RO JECTIO N ...................................................... 87 2.5.3.2 GRAY C O D E S ............................................................... 88 2.5.3.3 FRINGE P RO JE C TIO N ..................................................... 90 2.5.3.4 HYBRID S Y STE M S......................................................... 91 2.5.4 TIME-OF-FLIGHT C AM ERAS............................................................ 91 2.5.4.1 CONTINUOUS-WAVE-MODULATED TIME-OF-FLIGHT C AM ERAS..................................................................... 92 2.5.4.2 PULSE-MODULATED TIME-OF-FLIGHT C A M E RA S ............. 93 3 M ACHINE V ISION A LG O RITH M S 97 3.1 FUNDAMENTAL DATA S TRU CTU RES............................................................... 97 3.1.1 IM AG ES........................................................................................ 97 3.1.2 R E G IO N S ..................................................................................... 98 3.1.3 SUBPIXEL-PRECISE C O N TO U R S ......................................................... 101 3.2 IMAGE ENHANCEM ENT..................................................................................101 3.2.1 GRAY VALUE TRANSFORMATIONS ......................................................102 3.2.1.1 CONTRAST ENHANCEMENT ............................................... 102 3.2.1.2 CONTRAST N ORM ALIZATION ............................................... 102 3.2.1.3 ROBUST CONTRAST N ORM ALIZATION...................................103 3.2.2 RADIOMETRIC CALIBRATION ............................................................... 105 3.2.2.1 CHART-BASED RADIOMETRIC CALIBRATION ......................... 105 3.2.2.2 CHARTLESS RADIOMETRIC C A LIB RA TIO N ............................ 106 3.2.3 IMAGE S M O O TH IN G .........................................................................110 3.2.3.1 TEMPORAL AVERAGING ..................................................... I L L 3.2.3.2 MEAN F I L T E R ..................................................................112 3.2.3.3 BORDER TREATMENT OF F ILTE RS .........................................113 3.2.3.4 RUNTIME COMPLEXITY OF F I L T E R S ................................... 114 3.2.3.5 LINEAR F I L T E R S .............................................................. 115 3.2.3.6 FREQUENCY RESPONSE OF THE MEAN FILTER ................... 116 3.23.1 GAUSSIAN FILTER ............................................................ 117 3.2.3.8 NOISE SUPPRESSION BY LINEAR FILTERS .........................118 3.2.3.9 MEDIAN AND RANK F I L T E R S ............................................ 119 3.2.4 FOURIER TRANSFORM .........................................................................120 3.2.4.1 CONTINUOUS FOURIER T RA N SFO RM .................................. 120 3.2.4.2 DISCRETE FOURIER T RANSFORM ......................................... 123 3.3 GEOMETRIC TRANSFORMATIONS ..................................................................... 126 3.3.1 AFFINE T RANSFORM ATIONS................................................................126 3.3.1.1 PROJECTIVE TRANSFORM ATIONS ........................................ 127 3.3.2 IMAGE T RANSFORM ATIONS................................................................128 3.3.2.1 NEAREST-NEIGHBOR INTERPOLATION .................................. 128 3.3.2.2 BILINEAR IN TERPOLATION................................................... 129 3.3.2.3 BICUBIC IN TERP O LATIO N .................................................. 130 3.3.2.4 SMOOTHING TO AVOID A LIA SIN G ......................................132 3.3.3 PROJECTIVE IMAGE TRANSFORMATIONS .............................................133 3.3.4 POLAR TRANSFORMATIONS...................................................................133 3.4 IMAGE SEGMENTATION............................................................................... 135 3.4.1 THRESHOLDING ............................................................................... 135 3.4.1.1 GLOBAL THRESHOLDING .................................................. 135 3.4.1.2 AUTOMATIC THRESHOLD S ELECTIO N ...................................136 3.4.1.3 DYNAMIC T H RESH O LD IN G ............................................... 138 3.4.1.4 VARIATION M O D E L............................................................141 3.4.2 EXTRACTION OF CONNECTED COMPONENTS ...................................... 144 3.4.3 SUBPIXEL-PRECISE T HRESH O LD IN G ................................................... 147 3.5 FEATURE EXTRACTION......................................................................................149 3.5.1 REGION F E A TU RE S............................................................................ 149 3.5.1.1 A R E A ...............................................................................149 3.5.1.2 MOMENTS ..................................................................... 150 3.5.1.3 ELLIPSE PARAM ETERS.........................................................151 3.5.1.4 ENCLOSING RECTANGLES AND C IRCLES ............................... 153 3.5.1.5 CONTOUR L E N G TH ............................................................154 3.5.2 GRAY VALUE F EATURES......................................................................154 3.5.2.1 STATISTICAL FEATURES ..................................................... 154 3.5.2.2 MOMENTS ..................................................................... 155 3.5.2.3 ELLIPSE PARAM ETERS.........................................................155 3.5.2.4 COMPARISON OF REGION AND GRAY VALUE MOMENTS . 155 3.5.3 CONTOUR F E A TU R E S ......................................................................... 158 3.5.3.1 CONTOUR LENGTH, ENCLOSING RECTANGLES AND CIRCLES . 158 3.5.3.2 MOMENTS ..................................................................... 158 3.6 M ORPHOLOGY ........................................................................................... 159 3.6.1 REGION M ORPHOLOGY......................................................................159 3.6.1.1 SET O PERATIONS............................................................... 159 3.6.1.2 MINKOWSKI ADDITION AND D ILATION ............................... 161 3.6.1.3 MINKOWSKI SUBTRACTION AND E R O S IO N ......................... 163 3.6.1.4 REGION B O U N D A RIE S ...................................................... 166 3.6.1.5 HIT-OR-MISS T RA N S FO RM ............................................... 167 3.6.1.6 OPENING AND C LO S IN G .................................................. 168 3.6.1.7 S K ELETO N ........................................................................ 172 3.6.1.8 DISTANCE T RAN SFO RM ..................................................... 173 3.6.2 GRAY VALUE MORPHOLOGY ............................................................ 175 3.6.2.1 MINKOWSKI ADDITION AND D ILATION ............................... 175 3.6.2.2 MINKOWSKI SUBTRACTION AND E R O S IO N ......................... 176 3.6.2.3 OPENING AND C LO S IN G .................................................. 177 3.6.2.4 MORPHOLOGICAL G RADIENT ............................................... 179 3.7 EDGE E X TRACTIO N .....................................................................................180 3.7.1 DEFINITION OF E D G E S .................................................................. 180 3.7.1.1 DEFINITION OF EDGES IN I D ......................................... 180 3.7.1.2 DEFINITION OF EDGES IN 2 D .............................................181 3.7.2 ID EDGE E X TRACTIO N ......................................................................183 3.7.2.1 DISCRETE DERIVATIVES .................................................. 184 3.12.2 SMOOTHING PERPENDICULAR TO A P RO FILE..........................185 3.12.3 OPTIMAL EDGE FILTERS ...................................................186 3.7.2.4 PIXEL-ACCURATE EDGE EXTRACTION .................................... 188 3.7.2.5 SUBPIXEL-ACCURATE EDGE EXTRACTION.............................189 3.7.3 2D EDGE E X TRACTIO N ......................................................................189 3.7.3.1 DISCRETE DERIVATIVES.......................................................190 3.1.32 OPTIMAL EDGE FILTERS ....................................................191 3.7.3.3 NON-MAXIMUM SUPPRESSION ...................................... 192 3.7.3.4 HYSTERESIS THRESHOLDING................................................194 3.7.3.5 SUBPIXEL-ACCURATE EDGE EXTRACTION ......................... 194 3.7.4 ACCURACY AND PRECISION OF E D G E S ................................................196 3.7.4.1 DEFINITION OF ACCURACY AND PRECISION..........................197 3.7.4.2 ANALYTICAL EDGE ACCURACY AND P RE C ISIO N ................... 198 3.1 A.3 EDGE ACCURACY AND PRECISION ON REAL IMAGES . . . 199 3.8 SEGMENTATION AND FITTING OF GEOMETRIC P RIM ITIVES ............................ 203 3.8.1 FITTING L IN E S ..................................................................................204 3.8.1.1 LEAST-SQUARES LINE F I T T I N G .........................................204 3.8.1.2 ROBUST LINE F I T T I N G ..................................................... 205 3.8.2 FITTING C IRC LE S ...............................................................................208 3.8.2.1 LEAST-SQUARES CIRCLE FITTING.........................................208 3.8.2.2 ROBUST CIRCLE F I T T I N G .................................................. 209 3.8.3 FITTING ELLIPSES ........................................................................... 210 3.8.3.1 LEAST-SQUARES ELLIPSE F ITTIN G ......................................210 3.8.3.2 ROBUST ELLIPSE F ITTIN G .................................................. 210 3.8.4 SEGMENTATION OF CONTOURS............................................................211 3.8.4.1 SEGMENTATION OF CONTOURS INTO L IN E S ......................... 211 3.8.4.2 SEGMENTATION OF CONTOURS INTO LINES, CIRCLES, AND E L LIP S E S ........................................................................ 213 3.9 CAMERA C ALIB RATIO N .............................................................................. 215 3.9.1 CAMERA MODELS FOR AREA SCAN CAMERAS WITH REGULAR LENSES 216 3.9.1.1 EXTERIOR O RIENTATION..................................................... 217 3.9.1.2 PROJECTION FROM 3D TO 2 D ........................................... 219 3.9.1.3 LENS D ISTORTIONS ........................................................... 219 3.9.1.4 IMAGE COORDINATES .....................................................221 3.9.2 CAMERA MODELS FOR AREA SCAN CAMERAS WITH TILT LENSES . . 222 3.9.2.1 LENS D ISTORTIONS........................................................... 222 3.9.2.2 MODELING THE POSE OF THE TILTED IMAGE PLANE .... 222 3.9.2.3 IMAGE-SPACE TELECENTRIC L E N S E S ............................... 223 3.9.2.4 IMAGE-SPACE PERSPECTIVE L E N S E S ............................... 224 3.9.3 CAMERA MODEL FOR LINE SCAN C A M E RA S ...................................... 225 3.9.3.1 CAMERA M O TIO N ............................................................ 225 3.9.3.2 EXTERIOR O RIENTATION......................................................226 3.9.3.3 INTERIOR O RIE N TA TIO N ......................................................227 3.9.3.4 NONLINEARITIES OF THE LINE SCAN CAMERA MODEL . . . 229 3.9.4 CALIBRATION P R O C E S S ..................................................................... 230 3.9.4.1 CALIBRATION T A RG E T.........................................................230 3.9.4.2 SINGLE-IMAGE CAMERA CALIBRATION................................232 3.9.4.3 DEGENERACIES WHEN CALIBRATING WITH A SINGLE IMAGE 233 3.9A 4 MULTI-IMAGE CAMERA C ALIB RATIO N ................................234 3.9.4.5 DEGENERACIES OCCURRING WITH TILT L E N S E S ............. 234 3.9.4.6 EXCLUDING PARAMETERS FROM THE OPTIMIZATION . . . 235 3.9.5 WORLD COORDINATES FROM SINGLE IM AGES ...................................... 235 3.9.5.1 TELECENTRIC C AM ERAS......................................................236 3.9.5.2 PERSPECTIVE CAMERAS ...................................................236 3.9.5.3 LINE-SCAN C A M E RA S ......................................................238 3.9.5.4 IMAGE R ECTIFICATION......................................................238 3.9.6 ACCURACY OF THE CAMERA P A RA M E TE RS ......................................... 238 3.9.6.1 INFLUENCE OF THE NUMBER OF CALIBRATION IMAGES ON THE ACCURACY ............................................................... 239 3.9.6.2 INFLUENCE OF THE FOCUS SETTING ON THE CAMERA PARAM ETERS ..................................................................... 240 3.9.6.3 INFLUENCE OF THE DIAPHRAGM SETTING ON THE CAMERA PARAM ETERS......................................................................240 3.10 3D RECONSTRUCTION .................................................................................. 241 3.10.1 STEREO RECONSTRUCTION .................................................................. 241 3.10.1.1 STEREO GEOMETRY .........................................................242 3.10.1.2 STEREO C ALIBRATION.........................................................243 3.10.1.3 EPIPOLAR G E O M E TRY ......................................................244 3.10.1.4 IMAGE R ECTIFICATION......................................................247 3.10.1.5 D ISPARITY.........................................................................249 3.10.1.6 STEREO M ATCHING............................................................ 250 3.10.1.7 EFFECT OF WINDOW SIZE ............................................... 252 3.10.1.8 ROBUST STEREO M ATCHING ............................................... 253 3.10.1.9 SPACETIME STEREO M ATCH IN G ......................................... 254 3.10.2 SHEET OF LIGHT RECONSTRUCTION......................................................254 3.10.2.1 EXTRACTION OF THE LASER LINE ...................................... 255 3.10.2.2 SENSOR CALIBRATION AND 3D R ECONSTRUCTION ............. 256 3.10.3 STRUCTURED LIGHT R ECO N STRU CTIO N ............................................... 257 3.10.3.1 DECODING THE S T R I P E S ...................................................257 3.10.3.2 SENSOR CALIBRATION AND 3D R ECONSTRUCTION ................ 258 3.11 TEMPLATE M A TC H IN G .................................................................................. 262 3.11.1 GRAY-VALUE-BASED TEMPLATE M A TC H IN G .................................... 263 3.11.1.1 SIMILARITY MEASURES BASED ON GRAY VALUE DIFFERENCES .................................................................. 263 3.11.1.2 NORMALIZED C ROSS-C ORRELATION...................................264 3.11.1.3 EFFICIENT EVALUATION OF THE SIMILARITY MEASURES . . 266 3.11.2 MATCHING USING IMAGE P Y RAM ID S ............................................... 267 3.11.2.1 IMAGE P Y RAM ID S............................................................268 3.11.2.2 HIERARCHICAL S E A R C H ......................................................270 3.11.3 SUBPIXEL-ACCURATE GRAY-VALUE-BASED M A TC H IN G ...................... 271 3.11.4 TEMPLATE MATCHING WITH ROTATIONS AND S CALIN G S ...................... 272 3.11.5 ROBUST TEMPLATE M A TC H IN G .........................................................273 3.11.5.1 MEAN SQUARED EDGE D ISTANCE......................................274 3.11.5.2 HAUSDORFF D IS TA N C E ..................................................... 276 3.11.5.3 GENERALIZED HOUGH T RA N SFO RM ...................................277 3.11.5.4 GEOMETRIC H A S H IN G ..................................................... 281 3.11.5.5 MATCHING GEOMETRIC PRIM ITIVES...................................283 3.11.5.6 SHAPE-BASED M A TC H IN G ............................................... 286 3.12 3D OBJECT R ECO G N ITIO N ........................................................................ 292 3.12.1 DEFORMABLE M ATCHING.................................................................. 293 3.12.1.1 PRINCIPLE OF DEFORMABLE M A TC H IN G ............................ 293 3.12.1.2 MODEL G ENERATION.........................................................295 3.12.1.3 SIMILARITY M E A S U R E ......................................................295 3.12.1.4 HIERARCHICAL S E A R C H ......................................................297 3.12.1.5 LEAST-SQUARES POSE REFINEM ENT...................................297 3.12.1.6 3D POSE E STIM ATIO N ......................................................297 3.12.1.7 RECOGNITION OF LOCALLY DEFORMED O B JE C TS ................ 300 3.12.2 SHAPE-BASED 3D M A TC H IN G .........................................................302 3.12.2.1 VIEW-BASED APPROACH ............................................... 303 3.12.2.2 RESTRICTING THE POSE R A N G E ......................................... 306 3.12.2.3 HIERARCHICAL M O D E L ......................................................307 3.12.2.4 2D MODEL G ENERATION .................................................. 308 3.12.2.5 PERSPECTIVE C O RRE C TIO N ............................................... 310 3.12.2.6 LEAST-SQUARES POSE R EFINEM ENT...................................311 3.12.2.7 E X A M P LE S ..................................................................... 312 3.12.3 SURFACE-BASED 3D MATCHING ......................................................313 3.12.3.1 GLOBAL MODEL DESCRIPTION ............................................ 314 3.12.3.2 LOCAL P A RA M E TE RS.........................................................316 3.12.3.3 V O TIN G ........................................................................... 318 3.12.3.4 LEAST-SQUARES POSE REFINEM ENT...................................319 3.12.3.5 EXTENSION FOR RECOGNIZING DEFORMED OBJECTS . . . 321 3.12.3.6 EXTENSION FOR MULTIMODAL D A TA ...................................321 3.13 HAND-EYE CALIBRATION ........................................................................... 323 3.13.1 INTRODUCTION..................................................................................323 3.13.2 PROBLEM D E FIN ITIO N ..................................................................... 325 3.13.3 DUAL QUATERNIONS AND SCREW T H E O RY ......................................... 327 3.13.3.1 Q UATERNIONS.................................................................. 327 3.13.3.2 S CREW S ........................................................................... 329 3.13.3.3 DUAL N UM BERS...............................................................330 3.13.3.4 DUAL Q U ATERN IO N S.......................................................330 3.13.4 LINEAR HAND-EYE C ALIBRATION......................................................331 3.13.5 NONLINEAR HAND-EYE C ALIB RATIO N ............................................... 334 3.13.6 HAND-EYE CALIBRATION OF SCARA R O B O T S ................................335 3.14 OPTICAL CHARACTER R E C O G N ITIO N ............................................................... 337 3.14.1 CHARACTER SEGM ENTATION ............................................................... 338 3.14.2 FEATURE EXTRACTION.........................................................................339 3.15 C LASSIFICATIO N ............................................................................................342 3.15.1 DECISION T HEORY............................................................................343 3.15.1.1 BAYES DECISION RULE ...................................................343 3.15.1.2 CLASSIFIER T Y P E S ............................................................ 345 3.15.1.3 TRAINING, TEST, AND VALIDATION S E T S .............................345 3.15.1.4 NOVELTY D ETECTION......................................................... 345 3.15.2 CLASSIFIERS BASED ON ESTIMATING CLASS P RO B A B ILITIE S ............. 346 3.15.2.1 K NEAREST-NEIGHBOR C LA SSIFIE RS ................................... 347 3.15.2.2 GAUSSIAN MIXTURE MODEL C LA SSIFIE RS ......................... 347 3.15.3 CLASSIFIERS BASED ON CONSTRUCTING SEPARATING HYPERSURFACES 350 3.15.3.1 SINGLE-LAYER P ERCEP TRO N S ............................................ 350 3.15.3.2 MULTILAYER PERCEPTRONS ............................................... 352 3.15.3.3 SUPPORT VECTOR M ACH IN ES ............................................ 358 3.15.3.4 CONVOLUTIONAL NEURAL N ETWORKS...................................365 3.15.4 EXAMPLE OF USING CLASSIFIERS FOR O C R ...................................... 369 4 M ACHINE V ISION A P P LIC A TIO N S 371 4.1 WAFER D I C I N G ............................................................................................371 4.1.1 DETERMINING THE WIDTH AND HEIGHT OF THE D IE S ......................... 372 4.1.2 DETERMINING THE POSITION OF THE D I E S ........................................ 374 4.1.3 EXERCISES ..................................................................................... 376 4.2 READING OF SERIAL N U M B E RS ..................................................................... 377 4.2.1 RECTIFYING THE IMAGE USING A POLAR T RANSFORM ATION ................ 377 4.2.2 SEGMENTING THE C H A RA C TE RS.........................................................380 4.2.3 READING THE C H A RA C TE RS ............................................................... 382 4.2.4 EXERCISES ..................................................................................... 382 4.3 INSPECTION OF SAW B LA D E S .........................................................................383 4.3.1 EXTRACTING THE SAW BLADE C O N TO U R ............................................ 384 4.3.2 EXTRACTING THE TEETH OF THE SAW B L A D E ...................................... 385 4.3.3 MEASURING THE ANGLES OF THE TEETH OF THE SAW B LADE ................ 386 4.3.4 E X E RC ISE ........................................................................................ 388 4.4 PRINT IN SP E C TIO N ........................................................................................ 388 4.4.1 CREATING THE MODEL OF THE CORRECT PRINT ON THE RELAY .... 389 4.4.2 CREATING THE MODEL TO ALIGN THE R ELAYS ...................................... 390 4.4.3 PERFORMING THE PRINT INSPECTION...................................................391 4.4.4 EXERCISES ..................................................................................... 392 4.5 INSPECTION OF BALL GRID A R R A Y S ............................................................... 392 4.5.1 FINDING BALLS WITH SHAPE D EFECTS .............................................. 393 4.5.2 CONSTRUCTING A GEOMETRIC MODEL OF A CORRECT B G A ............. 395 4.5.3 FINDING MISSING AND EXTRANEOUS B A L L S ......................................397 4.5.4 FINDING DISPLACED B A LLS ............................................................... 398 4.5.5 EXERCISES .....................................................................................400 4.6 SURFACE INSPECTION..................................................................................400 4.6.1 SEGMENTING THE DOORKNOB .........................................................401 4.6.2 FINDING THE SURFACE TO INSPECT .................................................. 402 4.6.3 DETECTING D E FE C TS ........................................................................ 405 4.6.4 EXERCISES .....................................................................................407 4.7 MEASUREMENT OF SPARK P L U G S ..................................................................408 4.7.1 CALIBRATING THE C A M E RA ...............................................................409 4.7.2 DETERMINING THE POSITION OF THE SPARK PLUG ............................ 410 4.7.3 PERFORMING THE M EASUREM ENT ..................................................... 411 4.7.4 EXERCISES .....................................................................................413 4.8 MOLDING FLASH D E TE C TIO N ........................................................................ 414 4.8.1 MOLDING FLASH DETECTION USING REGION MORPHOLOGY .... 414 4.8.2 MOLDING FLASH DETECTION WITH SUBPIXEL-PRECISE CONTOURS . . 418 4.8.3 E X E RC ISE ........................................................................................ 421 4.9 INSPECTION OF PUNCHED S H E E TS .................................................................. 421 4.9.1 EXTRACTING THE BOUNDARIES OF THE PUNCHED SHEETS ................... 422 4.9.2 PERFORMING THE INSPECTION............................................................424 4.9.3 EXERCISES .....................................................................................425 4.10 3D PLANE RECONSTRUCTION WITH S TE RE O .................................................. 425 4.10.1 CALIBRATING THE STEREO SETUP .................................................. 426 4.10.2 PERFORMING THE 3D RECONSTRUCTION AND IN SP ECTIO N ................... 428 4.10.3 E X E RC ISE ........................................................................................ 432 4.11 POSE VERIFICATION OF RESISTORS.................................................................. 432 4.11.1 CREATING MODELS OF THE R E SISTO RS ............................................... 433 4.11.2 VERIFYING THE POSE AND TYPE OF THE R ESISTORS ............................ 436 4.11.3 EXERCISES .....................................................................................438 4.12 CLASSIFICATION OF NON-WOVEN F A B RIC S ..................................................... 438 4.12.1 TRAINING THE CLASSIFIER.................................................................. 438 4.12.2 PERFORMING THE TEXTURE CLASSIFICATION.........................................440 4.12.3 E X E RC ISE ........................................................................................443 4.13 SURFACE COM PARISON..................................................................................443 4.13.1 CREATING THE REFERENCE MODEL .................................................. 443 4.13.2 RECONSTRUCTING AND ALIGNING O BJECTS.........................................445 4.13.3 COMPARING OBJECTS AND CLASSIFYING E RRO RS ............................... 445 4.13.4 E X E RC ISE ........................................................................................450 4.14 3D PICK-AND-PLACE.....................................................................................451 4.14.1 PERFORMING THE HAND-EYE C A LIB RA TIO N ......................................452 4.14.2 DEFINING THE GRASPING P O IN T ........................................................ 455 4.14.3 PICKING AND PLACING OBJECTS ..................................................... 457 4.14.4 EXERCISES .....................................................................................458 REFERENCES 461 INDEX 475
any_adam_object	1
author	Steger, Carsten Ulrich, Markus Wiedemann, Christian
author_facet	Steger, Carsten Ulrich, Markus Wiedemann, Christian
author_role	aut aut aut
author_sort	Steger, Carsten
author_variant	c s cs m u mu c w cw
building	Verbundindex
bvnumber	BV044670270
classification_rvk	ST 330
classification_tum	DAT 760f
ctrlnum	(OCoLC)1022089388 (DE-599)DNB1140659693
dewey-full	530
dewey-hundreds	500 - Natural sciences and mathematics
dewey-ones	530 - Physics
dewey-raw	530
dewey-search	530
dewey-sort	3530
dewey-tens	530 - Physics
discipline	Physik Informatik
edition	2nd, completely revised and enlarged edition
format	Book
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02805nam a22007218c 4500</leader><controlfield tag="001">BV044670270</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20200701 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">171206s2018 gw a\|\|\| \|\|\|\| 00\|\|\| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">17,N41</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1140659693</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783527413652</subfield><subfield code="c">pbk</subfield><subfield code="9">978-3-527-41365-2</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9783527413652</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">Bestellnummer: 1141365 000</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1022089388</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1140659693</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-BW</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-M347</subfield><subfield code="a">DE-1050</subfield><subfield code="a">DE-11</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-634</subfield><subfield code="a">DE-91G</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-861</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">530</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 330</subfield><subfield code="0">(DE-625)143663:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 760f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">530</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Steger, Carsten</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine vision algorithms and applications</subfield><subfield code="c">Carsten Steger, Markus Ulrich, and Christian Wiedemann</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">2nd, completely revised and enlarged edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Weinheim</subfield><subfield code="b">Wiley-VCH</subfield><subfield code="c">[2018]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xxii, 494 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Sehen</subfield><subfield code="0">(DE-588)4129594-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mustererkennung</subfield><subfield code="0">(DE-588)4040936-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Algorithmen u. Datenstrukturen</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Algorithms & Data Structures</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bildgebende Systeme u. Verfahren</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bildgebendes Verfahren</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Computer Science</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Electrical & Electronics Engineering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Elektrotechnik u. Elektronik</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Imaging Systems & Technology</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Informatik</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Maschinelles Sehen</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Mustererkennung</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Optics & Photonics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Optik u. Photonik</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Physics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Physik</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Sehen</subfield><subfield code="0">(DE-588)4129594-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Mustererkennung</subfield><subfield code="0">(DE-588)4040936-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ulrich, Markus</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wiedemann, Christian</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Wiley-VCH</subfield><subfield code="0">(DE-588)16179388-5</subfield><subfield code="4">pbl</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, PDF</subfield><subfield code="z">978-3-527-81290-5</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, EPUB</subfield><subfield code="z">978-3-527-81289-9</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, MOBI</subfield><subfield code="z">978-3-527-81291-2</subfield></datafield><datafield tag="780" ind1="0" ind2="0"><subfield code="i">Vorangegangen ist</subfield><subfield code="z">9783527407347</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">X:MVB</subfield><subfield code="u">http://www.wiley-vch.de/publish/dt/books/ISBN978-3-527-41365-2/</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">DNB Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030067628&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030067628</subfield></datafield></record></collection>
id	DE-604.BV044670270
illustrated	Illustrated
indexdate	2024-07-10T07:58:51Z
institution	BVB
institution_GND	(DE-588)16179388-5
isbn	9783527413652
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-030067628
oclc_num	1022089388
open_access_boolean
owner	DE-M347 DE-1050 DE-11 DE-355 DE-BY-UBR DE-29T DE-634 DE-91G DE-BY-TUM DE-573 DE-703 DE-898 DE-BY-UBR DE-861
owner_facet	DE-M347 DE-1050 DE-11 DE-355 DE-BY-UBR DE-29T DE-634 DE-91G DE-BY-TUM DE-573 DE-703 DE-898 DE-BY-UBR DE-861
physical	xxii, 494 Seiten Illustrationen, Diagramme
publishDate	2018
publishDateSearch	2018
publishDateSort	2018
publisher	Wiley-VCH
record_format	marc
spelling	Steger, Carsten Verfasser aut Machine vision algorithms and applications Carsten Steger, Markus Ulrich, and Christian Wiedemann 2nd, completely revised and enlarged edition Weinheim Wiley-VCH [2018] xxii, 494 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Algorithmen u. Datenstrukturen Algorithms & Data Structures Bildgebende Systeme u. Verfahren Bildgebendes Verfahren Computer Science Electrical & Electronics Engineering Elektrotechnik u. Elektronik Imaging Systems & Technology Informatik Maschinelles Sehen Mustererkennung Optics & Photonics Optik u. Photonik Physics Physik Maschinelles Sehen (DE-588)4129594-8 s Mustererkennung (DE-588)4040936-3 s DE-604 Ulrich, Markus Verfasser aut Wiedemann, Christian Verfasser aut Wiley-VCH (DE-588)16179388-5 pbl Erscheint auch als Online-Ausgabe, PDF 978-3-527-81290-5 Erscheint auch als Online-Ausgabe, EPUB 978-3-527-81289-9 Erscheint auch als Online-Ausgabe, MOBI 978-3-527-81291-2 Vorangegangen ist 9783527407347 X:MVB http://www.wiley-vch.de/publish/dt/books/ISBN978-3-527-41365-2/ DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030067628&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Steger, Carsten Ulrich, Markus Wiedemann, Christian Machine vision algorithms and applications Maschinelles Sehen (DE-588)4129594-8 gnd Mustererkennung (DE-588)4040936-3 gnd
subject_GND	(DE-588)4129594-8 (DE-588)4040936-3
title	Machine vision algorithms and applications
title_auth	Machine vision algorithms and applications
title_exact_search	Machine vision algorithms and applications
title_full	Machine vision algorithms and applications Carsten Steger, Markus Ulrich, and Christian Wiedemann
title_fullStr	Machine vision algorithms and applications Carsten Steger, Markus Ulrich, and Christian Wiedemann
title_full_unstemmed	Machine vision algorithms and applications Carsten Steger, Markus Ulrich, and Christian Wiedemann
title_short	Machine vision algorithms and applications
title_sort	machine vision algorithms and applications
topic	Maschinelles Sehen (DE-588)4129594-8 gnd Mustererkennung (DE-588)4040936-3 gnd
topic_facet	Maschinelles Sehen Mustererkennung
url	http://www.wiley-vch.de/publish/dt/books/ISBN978-3-527-41365-2/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030067628&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT stegercarsten machinevisionalgorithmsandapplications AT ulrichmarkus machinevisionalgorithmsandapplications AT wiedemannchristian machinevisionalgorithmsandapplications AT wileyvch machinevisionalgorithmsandapplications

Holdings

There is no print copy available.

Interlibrary loan Place Request Caution: Not in THWS collection! Indexes

MARC

Record in the Search Index

There is no print copy available.

Similar Items