Intelligent machine vision: techniques, implementations and applications
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London [u.a.]
Springer
2001
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009328349&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXIV, 392, 24 S. Ill., graph. Darst. |
| ISBN: | 3540762248 |
Internformat
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| 084 | |a DAT 760f |2 stub | ||
| 084 | |a 24 |2 ssgn | ||
| 100 | 1 | |a Batchelor, Bruce G. |d 1943- |e Verfasser |0 (DE-588)122561279 |4 aut | |
| 245 | 1 | 0 | |a Intelligent machine vision |b techniques, implementations and applications |c Bruce Batchelor and Frederick Waltz |
| 264 | 1 | |a London [u.a.] |b Springer |c 2001 | |
| 300 | |a XXIV, 392, 24 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Maschinelles Sehen | |
| 650 | 4 | |a Maschinelles Sehen - Graphische Programmierung | |
| 650 | 4 | |a Computer vision | |
| 650 | 4 | |a Image processing |x Digital techniques | |
| 650 | 0 | 7 | |a Graphische Programmierung |0 (DE-588)4337137-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Sehen |0 (DE-588)4129594-8 |2 gnd |9 rswk-swf |
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| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Maschinelles Sehen |0 (DE-588)4129594-8 |D s |
| 689 | 1 | 1 | |a Graphische Programmierung |0 (DE-588)4337137-1 |D s |
| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Waltz, Frederick |e Verfasser |4 aut | |
| 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=009328349&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-009328349 | |
Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 DAT 760f 2001 A 6923 |
|---|---|
| DE-BY-TUM_katkey | 1239402 |
| DE-BY-TUM_location | 01 |
| DE-BY-TUM_media_number | 040020276331 |
| _version_ | 1821932116829536256 |
| adam_text |
CONTENTS
CONTENTS
.
VII
PREFACE
.
XV
ACKNOWLEDGMENTS
.
XX
GLOSSARY
OF
TERMS,
PRODUCTS,
AND
ACRONYMS
.
XXI
SOFTWARE
AND
HARDWARE
AVAILABILITY
.
XXIV
1
MACHINE
VISION
FOR
INDUSTRIAL
APPLICATIONS
.
1
1.1
NATURAL
AND
ARTIFICIAL
VISION
.
1
1.2
ARTIFICIAL
VISION
.
3
1.3
MACHINE
VISION
IS
NOT
COMPUTER
VISION
.
6
1.3.1
APPLICATIONS
.
9
1.3.2
DOES
IT
MATTER
WHAT
WE
CALL
THE
SUBJECT?
.
10
1.4
FOUR
CASE
STUDIES
.
12
1.4.1
DOOMED
TO
FAILURE
.
13
1.4.2
A
SUCCESSFUL
DESIGN
.
14
1.4.3
MOLDY
NUTS
.
14
1.4.4
A
SYSTEM
THAT
GREW
AND
GREW
.
15
1.5
MACHINE
VISION
IS
ENGINEERING,
NOT
SCIENCE
.
17
1.6
STRUCTURE,
DESIGN,
AND
USE
OF
MACHINE
VISION
SYSTEMS
.
18
1.6.1
USING
AN
IVS
FOR
PROBLEM
ANALYSIS
.
19
1.6.2
STRUCTURE
OF
AN
IVS
.
22
1.6.3
OTHER
USES
FOR
AN
IVS
.
22
1.7
OTHER
DESIGN
TOOLS
.
23
1.7.1
LIGHTING
ADVISOR
.
24
1.7.2
PROTOTYPING
KITS
FOR
IMAGE
ACQUISITION
.
26
1.8
OUTLINE
OF
THIS
BOOK
.
27
2
BASIC
MACHINE
VISION
TECHNIQUES
.
31
2.1
REPRESENTATIONS
OF
IMAGES
.
31
2.2
ELEMENTARY
IMAGE-PROCESSING
FUNCTIONS
.
33
2.2.1
MONADIC
PIXEL-BY-PIXEL
OPERATORS
.
33
2.2.2
DYADIC
PIXEL-BY-PIXEL
OPERATORS
.
35
2.2.3
LOCAL
OPERATORS
.
35
2.2.4
LINEAR
LOCAL
OPERATORS
.
36
2.2.5
NONLINEAR
LOCAL
OPERATORS
.
38
2.2.6
N-TUPLE
OPERATORS
.
40
2.2.7 EDGE
EFFECTS
.
41
2.2.8
INTENSITY
HISTOGRAM
[HPI,
HGI,
HGE,
HGC]
.
41
2.3
BINARY
IMAGES
.
42
2.3.1
MEASUREMENTS
ON
BINARY
IMAGES
.
47
2.3.2
SHAPE
DESCRIPTORS
.
48
VIII
INTELLIGENT
MACHINE
VISION
2.4
BINARY
MATHEMATICAL
MORPHOLOGY
.
49
2.4.1
OPENING
AND
CLOSING
OPERATIONS
.
51
2.4.2
STRUCTURING
ELEMENT
DECOMPOSITION
.
52
2.5
GREY-SCALE
MORPHOLOGY
.
53
2.6
GLOBAL
IMAGE
TRANSFORMS
.
55
2.6.1
HOUGH
TRANSFORM
.
56
2.6.2
TWO-DIMENSIONAL
DISCRETE
FOURIER
TRANSFORM
.
57
2.7
TEXTURE
ANALYSIS
.
58
2.7.1
STATISTICAL
APPROACHES
.
59
2.7.2
CO-OCCURRENCE
MATRIX
APPROACH
.
61
2.7.3
STRUCTURAL
APPROACHES
.
62
2.7.4
MORPHOLOGICAL
TEXTURE
ANALYSIS
.
63
2.8
FURTHER
REMARKS
.
63
3
ALGORITHMS,
APPROXIMATIONS,
AND
HEURISTICS
.
65
3.1
INTRODUCTION
.
65
3.1.1
ONE
ALGORITHM,
MANY
IMPLEMENTATIONS
.
67
3.1.2
DO
NOT
JUDGE
AN
ALGORITHM
BY
ITS
ACCURACY
ALONE
.
69
3.1.3
REFORMULATING
ALGORITHMS
TO
MAKE
IMPLEMENTATION
EASIER
.
71
3.1.4
USE
HEURISTICS
IN
LIEU
OF
ALGORITHMS
.
71
3.1.5
HEURISTICS
MAY
ACTUALLY
BE
ALGORITHMIC
.
72
3.1.6
PRIMARY
AND
SECONDARY
GOALS
.
72
3.1.7
EMOTIONAL
OBJECTIONS
TO
HEURISTICS
.
74
3.1.8
HEURISTICS
USED
TO
GUIDE
ALGORITHMS
.
74
3.1.9
HEURISTICS
MAY
COPE
WITH
A
WIDER
RANGE
OF
SITUATIONS
.
75
3.2
CHANGING
IMAGE
REPRESENTATION
.
75
3.2.1
CONVERTING
IMAGE
FORMAT
.
75
3.2.2
PROCESSING
A
BINARY
IMAGE
AS
A
GREY-SCALE
IMAGE
.
80
3.2.3
USING
IMAGES
AS
LOOKUP
TABLES
.
81
3.3
REDEFINING
ALGORITHMS
.
82
3.3.1
CONVOLUTION
OPERATIONS
.
82
3.3.2
MORE
ON
DECOMPOSITION
AND
ITERATED
OPERATIONS
.
86
3.3.3
SEPARATED
KERNEL
IMAGE
PROCESSING
USING
FINITE-STATE
MACHINES
.
87
3.3.4
BINARY
IMAGE
CODING
.
88
3.3.5
BLOB
CONNECTIVITY
ANALYSIS
.
89
3.3.6
CONVEX
HULL
.
92
3.3.7
EDGE
SMOOTHING
.
97
3.3.8
TECHNIQUES
BASED
ON
HISTOGRAMS
.
104
3.4
APPROXIMATE
AND
HEURISTIC
METHODS
.
108
3.4.1
MEASURING
DISTANCE
.
108
3.4.2
FITTING
CIRCLES
AND
POLYGONS
TO
A
BINARY
OBJECT
.
116
3.4.3
DETERMINING
OBJECT/FEATURE
ORIENTATION
.
123
3.5
ADDITIONAL
REMARKS
.
129
3.5.1
SOLVING
THE
RIGHT
PROBLEM
.
130
3.5.2
DEMOCRACY:
NO
SMALL
SUBSET
OF
OPERATORS
DOMINATES
.
130
CONTENTS
IX
3.5.3
LESSONS
OF
THIS
CHAPTER
.
131
4
SYSTEMS
ENGINEERING
.
135
4.1
INTERACTIVE
AND
TARGET
VISION
SYSTEMS
.
135
4.2
INTERACTIVE
VISION
SYSTEMS,
GENERAL
PRINCIPLES
.
136
4.2.1
SPEED
OF
OPERATION
.
136
4.2.2
COMMUNICATION
BETWEEN
AN
IVS
AND
ITS
USER
.
137
4.2.3
IMAGE
AND
TEXT
DISPLAYS
.
139
4.2.4
COMMAND-LINE
INTERFACES
.
141
4.2.5
HOW
MANY
IMAGES
DO
WE
NEED
TO
STORE
IN
RAM?
.
141
4.2.6
HOW
MANY
IMAGES
DO
WE
NEED
TO
DISPLAY?
.
143
4.3
PROLOG
IMAGE
PROCESSING
(PIP)
.
144
4.3.1
BASIC
COMMAND
STRUCTURE
.
145
4.3.2
DIALOG
BOX
.
155
4.3.3
PULL-DOWN
MENUS
.
157
4.3.4
EXTENDING
THE
PULL-DOWN
MENUS
.
158
4.3.5
COMMAND
KEYS
.
158
4.3.6
JOURNAL
WINDOW
.
159
4.3.7
NATURAL
LANGUAGE
INPUT
VIA
SPEECH
.
161
4.3.8
SPEECH
OUTPUT
.
165
4.3.9
CURSOR
.
165
4.3.10
ON-LINE
DOCUMENTATION
.
166
4.3.11
GENERATING
TEST
IMAGES
.
170
4.3.12
PIP
IS
NOT
JUST
AN
IMAGE-PROCESSING
SYSTEM
.
171
4.4
ADVANCED
ASPECTS
OF
PIP
.
171
4.4.1
PROGRAMMABLE
COLOR
FILTER
.
171
4.4.2
MATHEMATICAL
MORPHOLOGY
.
175
4.4.3
MULTIPLE-IMAGE-PROCESSING
PARADIGMS
.
179
4.4.4
IMAGE
STACK
AND
BACKTRACKING
.
180
4.4.5
PROGRAMMING
GENERIC
ALGORITHMS
.
181
4.4.6
BATCH
PROCESSING
OF
IMAGES
.
187
4.4.7
SIMULATING
A
LINE-SCAN
CAMERA
.
188
4.4.8
RANGE
MAPS
.
188
4.4.9
PROCESSING
IMAGE
SEQUENCES
.
189
4.4.10
INTERFACING
PIP
TO
THE
WORLD
WIDE
WEB
.
194
4.4.11
CONTROLLING
EXTERNAL
DEVICES
.
194
4.5
WINDOWS
IMAGE
PROCESSING
(WIP)
.
196
4.6
WEB-BASED
IMAGE
PROCESSING
(CIP)
.
198
4.7
TARGET
(FACTORY
FLOOR)
VISION
SYSTEMS
.
201
4.8
CONCLUDING
REMARKS
.
202
5
ALGORITHMS
AND
ARCHITECTURES
FOR
FAST
EXECUTION
.
203
5.1
CLASSIFICATION
OF
OPERATIONS
.
203
5.2
IMPLEMENTATION
OF
MONADIC
PIXEL-BY-PIXEL
OPERATIONS
.
204
5.3
IMPLEMENTATION
OF
DYADIC
PIXEL-BY-PIXEL
OPERATIONS
.
205
5.4
IMPLEMENTATION
OF
MONADIC
NEIGHBORHOOD
OPERATIONS
.
207
X
INTELLIGENT
MACHINE
VISION
5.5
IMPLEMENTATION
OF
MONADIC
GLOBAL
OPERATIONS
.209
5.6
SKIPSM
-
A
POWERFUL
IMPLEMENTATION
PARADIGM
.
211
5.6.1
SKIPSM
FUNDAMENTALS
.
212
5.6.2
SKIPSM
EXAMPLE
.
1
213
5.6.3
SKIPSM
EXAMPLE
.
2
216
5.6.4
ADDITIONAL
COMMENTS
ON
SKIPSM
IMPLEMENTATIONS
.
218
5.7
IMAGE-PROCESSING
ARCHITECTURES
.
218
5.8
SYSTEMS
WITH
RANDOM
ACCESS
TO
IMAGE
MEMORY
.
219
5.8.1
SYSTEMS
BASED
ON
PERSONAL
COMPUTERS
OR
WORKSTATIONS
.
219
5.8.2
SYSTEMS
BASED
ON
BIT-SLICE
PROCESSORS
.
221
5.8.3
SYSTEMS
BASED
ON
MULTIPLE
RANDOM-ACCESS
PROCESSORS
.
221
5.9
SYSTEMS
WITH
SEQUENTIAL
IMAGE
MEMORY
ACCESS
ONLY
.
224
5.9.1
CLASSIFICATION
OF
OPERATIONS
NEEDED
FOR
INSPECTION
.
224
5.9.2
PIPELINE
SYSTEMS
.
225
5.9.3
SINGLE-BOARD
SYSTEM
CAPABLE
OF
MANY
OPERATIONS
IN
ONE
PASS
.
226
5.9.4
HARDWARE
IMPLEMENTATIONS
OF
SKIPSM
.
228
5.10
SYSTEMS
FOR
CONTINUOUS
WEB-BASED
PROCESSING
.
228
6
ADDING
INTELLIGENCE
.
231
6.1
PRELIMINARY
REMARKS
.
231
6.1.1
BASIC
ASSUMPTIONS
.
231
6.2
IMPLEMENTING
IMAGE-PROCESSING
OPERATORS
.234
6.3
VERY
SIMPLE
PROLOG-1
(VSP)
.
238
6.3.1
DEFINING
THE
#
OPERATOR
.
240
6.3.2
DEVICE
CONTROL
.
242
6.3.3
VSP
CONTROL
SOFTWARE
.
243
6.4
PIP
.
251
6.4.1
SYSTEM
CONSIDERATIONS
.
251
6.4.2
WHY
NOT
IMPLEMENT
PROLOG-1
COMMANDS
DIRECTLY?
.
252
6.4.3
INFRASTRUCTURE
.
253
6.4.4
STORING,
DISPLAYING,
AND
MANIPULATING
IMAGES
.
254
6.4.5
PROLOG-C
INTERFACE
.
254
6.4.6
PIP
INFRASTRUCTURE
.
257
6.4.7
DEFINING
INFRASTRUCTURE
PREDICATES
.
259
6.4.8
IMPLEMENTING
PIP
'
S
BASIC
COMMANDS
.
262
6.5
WIP
.
265
6.5.1
WIP
DESIGN
PHILOSOPHY
.
265
6.5.2
HOST
APPLICATION
.
266
6.5.3
COMMAND
LINE
FUNCTION
.
268
6.5.4
PARSER
.
269
6.5.5
CALLING
FUNCTION
.
270
6.5.6
INTERFACE
FUNCTION
.
270
6.5.7
IMAGE
FUNCTION
.
270
6.5.8
RETURNING
RESULTS
AND
ERRORS
.
271
6.6
CONCLUDING
REMARKS
.
271
CONTENTS
XI
7
VISION
SYSTEMS
ON
THE
INTERNET
.
273
7.1
STAND-ALONE
AND
NETWORKED
SYSTEMS
.
273
7.2
JAVA
.
274
7.2.1
PLATFORM
INDEPENDENCE
.
274
7.2.2
APPLETS,
APPLICATIONS,
AND
SERVLETS
.
275
7.2.3
SECURITY
.
276
7.2.4
SPEED
.
IT!
7.2.5
INTERACTIVE
VISION
SYSTEMS
IN
JAVA
.
277
7.3
REMOTELY-OPERATED
PROTOTYPING
ENVIRONMENT
(ROPE)
.
278
7.3.1
WHAT
ROPE
DOES
.
279
7.3.2
ASSISTANT
.
280
7.3.3
VISION
ENGINEERS
SHOULD
AVOID
TRAVEL
.
282
7.3.4
CURRENT
VERSION
OF
ROPE
.
282
7.3.5
DIGITIZING
IMAGES
.
284
7.3.6
JAVA
INTERFACE
TO
THE
FLEXIBLE
INSPECTION
CELL
(JIFIC)
.
286
7.3.7
OTHER
FEATURES
OF
ROPE
.
287
7.4
CIP
.
288
7.4.1
STRUCTURE
AND
OPERATION
OF
CIP
.
292
7.5
REMARKS
.
293
8
VISUAL
PROGRAMMING
FOR
MACHINE
VISION
.
295
8.1
DESIGN
OUTLINE
.
295
8.1.1
GRAPHICAL
USER
INTERFACE
(GUI)
.
296
8.1.2
OBJECT-ORIENTED
PROGRAMMING
.
296
8.1.3
JAVA
IMAGE
HANDLING
.
297
8.1.4
IMAGE-PROCESSING
STRATEGY
.
298
8.2
DATA
TYPES
.
300
8.2.1
IMAGE
.
300
8.2.2
INTEGER
.
301
8.2.3
DOUBLE
.
301
8.2.4
BOOLEAN
.
301
8.2.5
STRING
.
302
8.2.6
INTEGER
ARRAY
.
302
8.3
NONLINEAR
FEEDBACK
BLOCKS
.
302
8.3.1
FEEDBACK
.
303
8.3.2
FOR
LOOP
.
304
8.3.3
IF
ELSE
.
305
8.3.4
NESTING
.
305
8.4
VISUAL
PROGRAMMING
ENVIRONMENT
.
305
8.4.1
INTERPRETATION
OF
GRAPHICS
FILES
.
306
8.4.2
PLUG-IN-AND-PLAY
ARCHITECTURE
.
307
8.5
IMAGE
VIEWER
AND
TOOLS
.
309
8.5.1
HORIZONTAL
AND
VERTICAL
SCANS
.
309
8.5.2
HISTOGRAMS
.
309
8.5.3
PSEUDOCOLOR
TABLES
.
309
XII
INTELLIGENT
MACHINE
VISION
8.5.4
3-D
PROFILE
VIEWER
.
310
8.6
SAMPLE
PROBLEMS
.
311
8.6.1
LOW-LEVEL
PROGRAMMING
.
311
8.6.2
HIGH-LEVEL
PROGRAMMING
.
312
8.6.3
CONVOLUTION
.
312
8.6.4
FOURIER
TRANSFORM
.
313
8.6.5
ISOLATE
THE
LARGEST
ITEM
IN
THE
FIELD
OF
VIEW
.
314
8.6.6
CHARACTER
DETECTION
USING
THE
N-TUPLE
OPERATOR
.
315
8.7
SUMMARY
.
315
9
APPLICATION
CASE
STUDIES
.
317
9.1
PRELIMINARY
REMARKS
.
317
9.2
TAKING
A
BROAD
VIEW
.
318
9.2.1
AUTOMOBILE
CONNECTING
ROD
(CONROD)
.
318
9.2.2
COFFEE
BEANS
.
319
9.2.3
TABLE
PLACE-SETTING
.
319
9.2.4
HYDRAULICS
MANIFOLD
.
320
9.2.5
HYDRAULICS
CYLINDER
FOR
AUTOMOBILE
BRAKE
SYSTEM
.
320
9.2.6
ELECTRICAL
CONNECTION
BLOCK
.
321
9.2.7
ELECTRIC
LIGHT
BULB
.
321
9.2.8
ANALYSIS
OF
INDUSTRIAL
X-RAYS
.
322
9.2.9
HIGHLY
VARIABLE
OBJECTS
(FOOD
AND
NATURAL
PRODUCTS)
.
323
9.3
CRACKS
IN
FERROUS
COMPONENTS
.
324
9.4
AEROSOL
SPRAY
CONE
.
325
9.5
GLASS
VIAL
.
326
9.6
COIN
.
327
9.7
METAL
GRID
.
328
9.8
TOROIDAL
METAL
COMPONENT
.
329
9.9
MAINS
POWER
PLUG
(X-RAY)
.
331
9.10
CONCLUSIONS
.
332
10
FINAL
REMARKS
.
335
10.1
INTERACTIVE
PROTOTYPING
SYSTEMS
.
336
10.2
TARGET
VISION
SYSTEMS
.
337
10.3
DESIGN
TOOLS
.
339
10.4
NETWORKED
SYSTEMS
.
340
10.5
SYSTEMS
INTEGRATION
.
341
10.6
ALGORITHMS
AND
HEURISTICS
.
342
10.7
CONCLUDING
COMMENTS
.
343
APPENDIX
A
PROGRAMMABLE
COLOR
FILTER
.
345
REPRESENTATION
OF
COLOR
.
345
COLOR
TRIANGLE
.
346
MAPPING
RGB
TO
HSI
.
348
PROGRAMMABLE
COLOR
FILTER
(PCF)
.
348
SOFTWARE
.
351
CONTENTS
XIII
APPENDIX
B
A
BRIEF
INTRODUCTION
TO
PROLOG
.
353
PROLOG
IS
DIFFERENT
.
353
DECLARATIVE
PROGRAMMING
.
353
FACTS
.
354
SIMPLE
QUERIES
.
354
RULES
.
355
QUERIES
INVOLVING
RULES
.
356
BACKTRACKING
AND
INSTANTIATION
.
356
RECURSION
.
357
LISTS
.
357
OTHER
FEATURES
.
358
FURTHER
READING
.
359
APPENDIX
C
PIP
COMMANDS
AND
THEIR
IMPLEMENTATION
.
361
REFERENCES
.
369
FURTHER
READING
.
374
INDEX
.
377 |
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| author | Batchelor, Bruce G. 1943- Waltz, Frederick |
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| dewey-full | 006.3/7 |
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| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/7 |
| dewey-search | 006.3/7 |
| dewey-sort | 16.3 17 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV013652672 |
| illustrated | Illustrated |
| indexdate | 2025-01-11T11:55:04Z |
| institution | BVB |
| isbn | 3540762248 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009328349 |
| oclc_num | 248309885 |
| open_access_boolean | |
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| physical | XXIV, 392, 24 S. Ill., graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Batchelor, Bruce G. 1943- Waltz, Frederick Intelligent machine vision techniques, implementations and applications Maschinelles Sehen Maschinelles Sehen - Graphische Programmierung Computer vision Image processing Digital techniques Graphische Programmierung (DE-588)4337137-1 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4337137-1 (DE-588)4129594-8 |
| title | Intelligent machine vision techniques, implementations and applications |
| title_auth | Intelligent machine vision techniques, implementations and applications |
| title_exact_search | Intelligent machine vision techniques, implementations and applications |
| title_full | Intelligent machine vision techniques, implementations and applications Bruce Batchelor and Frederick Waltz |
| title_fullStr | Intelligent machine vision techniques, implementations and applications Bruce Batchelor and Frederick Waltz |
| title_full_unstemmed | Intelligent machine vision techniques, implementations and applications Bruce Batchelor and Frederick Waltz |
| title_short | Intelligent machine vision |
| title_sort | intelligent machine vision techniques implementations and applications |
| title_sub | techniques, implementations and applications |
| topic | Maschinelles Sehen Maschinelles Sehen - Graphische Programmierung Computer vision Image processing Digital techniques Graphische Programmierung (DE-588)4337137-1 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Maschinelles Sehen Maschinelles Sehen - Graphische Programmierung Computer vision Image processing Digital techniques Graphische Programmierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009328349&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT batchelorbruceg intelligentmachinevisiontechniquesimplementationsandapplications AT waltzfrederick intelligentmachinevisiontechniquesimplementationsandapplications |
Inhaltsverzeichnis
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Paper/Kapitel scannen lassen
Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 DAT 760f 2001 A 6923 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |