85:, where the output is another image. The information extracted can be a simple good-part/bad-part signal, or more a complex set of data such as the identity, position and orientation of each object in an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, for security monitoring and vehicle guidance. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision is practically the only term used for these functions in industrial automation applications; the term is less universal for these functions in other environments such as security and vehicle guidance. Machine vision as a
97:; machine vision attempts to integrate existing technologies in new ways and apply them to solve real world problems in a way that meets the requirements of industrial automation and similar application areas. The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing. The primary uses for machine vision are automatic inspection and
202:
or image during the imaging process. A laser is projected onto the surfaces of an object. In machine vision this is accomplished with a scanning motion, either by moving the workpiece, or by moving the camera & laser imaging system. The line is viewed by a camera from a different angle; the deviation of the line represents shape variations. Lines from multiple scans are assembled into a
20:
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the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution. Many of the process steps are the same as with automatic inspection except with a focus on providing position and orientation information as the result.
368:
A common output from automatic inspection systems is pass/fail decisions. These decisions may in turn trigger mechanisms that reject failed items or sound an alarm. Other common outputs include object position and orientation information for robot guidance systems. Additionally, output types include
201:
Though the vast majority of machine vision applications are solved using two-dimensional imaging, machine vision applications utilizing 3D imaging are a growing niche within the industry. The most commonly used method for 3D imaging is scanning based triangulation which utilizes motion of the product
399:
Machine vision commonly provides location and orientation information to a robot to allow the robot to properly grasp the product. This capability is also used to guide motion that is simpler than robots, such as a 1 or 2 axis motion controller. The overall process includes planning the details of
306:
processing: weighted and self-training multi-variable decision making Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities. The most common result of such processing is classification. Examples of classification
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differentiation of defects. An example of "simple" differentiation is that the defects are dark and the good parts of the product are light. A common reason why some applications were not doable was when it was impossible to achieve the "simple"; deep learning removes this requirement, in essence
252:
Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that
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has variable meanings, most of which can be applied to techniques used in machine vision for over 20 years. However the usage of the term in "machine vision" began in the later 2010s with the advent of the capability to successfully apply such techniques to entire images in the industrial machine
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or a combination of these. Deep learning training and inference impose higher processing performance requirements. Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image,
113:
The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.; in this section the former is abbreviated as "automatic inspection". The overall process includes planning the details of the requirements and project, and then creating a solution. This
408:
As recently as 2006, one industry consultant reported that MV represented a $ 1.5 billion market in North
America. However, the editor-in-chief of an MV trade magazine asserted that "machine vision is not an industry per se" but rather "the integration of technologies and products that provide
358:
Comparison against target values to determine a "pass or fail" or "go/no go" result. For example, with code or bar code verification, the read value is compared to the stored target value. For gauging, a measurement is compared against the proper value and tolerances. For verification of
231:
followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include;
66:. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance.
391:"seeing" the object more as a human does, making it now possible to accomplish those automatic applications. The system learns from a large amount of images during a training phase and then executes the inspection during run-time use which is called "inference".
154:
or smart sensor. Inclusion of the full processing function into the same enclosure as the camera is often referred to as embedded processing. When separated, the connection may be made to specialized intermediate hardware, a custom processing appliance, or a
1338:
Lauren
Barghout. Visual Taxometric approach Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions. Information Processing and Management of Uncertainty in Knowledge-Based Systems. CCIS Springer-Verlag.
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numerical measurement data, data read from codes and characters, counts and classification of objects, displays of the process or results, stored images, alarms from automated space monitoring MV systems, and
359:
alpha-numberic codes, the OCR'd value is compared to the proper or target value. For inspection for blemishes, the measured size of the blemishes may be compared to the maximums allowed by quality standards.
31:) machine vision system Autovision II from 1983 being demonstrated at a trade show. Camera on tripod is pointing down at a light table to produce backlit image shown on screen, which is then subjected to
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69:
The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated
54:, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a
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HYBRID STRUCTURED LIGHT FOR SCALABLE DEPTH SENSING Yueyi Zhang, Zhiwei Xiong, Feng Wu
University of Science and Technology of China, Hefei, China Microsoft Research Asia, Beijing, China
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R.Morano, C.Ozturk, R.Conn, S.Dubin, S.Zietz, J.Nissano, "Structured light using pseudorandom codes", IEEE Transactions on
Pattern Analysis and Machine Intelligence 20 (3)(1998)322–327
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or point cloud. Stereoscopic vision is used in special cases involving unique features present in both views of a pair of cameras. Other 3D methods used for machine vision are
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317:. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size.
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Definitions of the term "Machine vision" vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to
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The imaging device (e.g. camera) can either be separate from the main image processing unit or combined with it in which case the combination is generally called a
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and grid based. One method is grid array based systems using pseudorandom structured light system as employed by the
Microsoft Kinect system circa 2012.
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vision space. Conventional machine vision usually requires the "physics" phase of a machine vision automatic inspection solution to create reliable
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The components of an automatic inspection system usually include lighting, a camera or other imager, a processor, software, and output devices.
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signals. This also includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange.
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services or applications that benefit true industries such as automotive or consumer goods manufacturing, agriculture, and defense."
198:, resolution, and whether or not the imaging process is simultaneous over the entire image, making it suitable for moving processes.
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1051:"Digital or Analog? Selecting the Right Camera for an Application Depends on What the Machine Vision System is Trying to Achieve"
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Principles, algorithms, Applications, Learning 5th
Edition by E.R. Davies Academic Press, Elselvier 2018 ISBN 978-0-12-809284-2
690:
134:
techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information.
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101:/process guidance. In more recent times the terms computer vision and machine vision have converged to a greater degree. See
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2004:
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167:). MV implementations also use digital cameras capable of direct connections (without a framegrabber) to a computer via
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to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.
292:: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks.
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of surfaces and X-ray imaging. Key differentiations within MV 2D visible light imaging are monochromatic vs. color,
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Color
Analysis: Identify parts, products and items using color, assess quality from color, and isolate
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By Fred Turek & Kim
Jackson Quality Magazine, March 2014 issue, Volume 53/Number 3 Pages 6-8
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After an image is acquired, it is processed. Central processing functions are generally done by a
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While conventional (2D visible light) imaging is most commonly used in MV, alternatives include
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and George C. Stockman (2001): “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall,
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section describes the technical process that occurs during the operation of the solution.
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Technology and methods used to provide imaging-based automatic inspection and analysis
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are object identification,"pass fail" classification of identified objects and OCR.
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Machine Vision - Automated Visual
Inspection: Theory, Practice and Applications
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Quality
Magazine May 2022 issue, Volume 61, Number 5 Published by BNP Media II
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by Mike Fussell Vision Systems Design magazine September 2019 issue pages 8-9
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within a computer using either an analog or standardized digital interface (
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Finding the optimal hardware for deep learining inference in machine vision
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inspection and analysis for such applications as automatic inspection,
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magazine published by Laurin Publishing Co. July 2019 issue Pages 60-64
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495:
Beyerer, JĂĽrgen; Puente LeĂłn, Fernando & Frese, Christian (2016).
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
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Industrial Image Processing: Visual Quality Control in Manufacturing
122:
The first step in the automatic inspection sequence of operation is
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http://research.microsoft.com/en-us/people/fengwu/depth-icip-12.pdf
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18:
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Computer and Machine Vision: Theory, Algorithms, Practicalities
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458:
Steger, Carsten; Markus Ulrich; Christian Wiedemann (2018).
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packages and programs developed in them then employ various
1510:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1485:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1460:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1432:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1374:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1349:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1295:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
1270:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
832:
Demant C.; Streicher-Abel B. & Waszkewitz P. (1999).
626:"Robot Vision vs Computer Vision: What's the Difference?"
73:
of the image and extraction of the required information.
994:"Cameras certified as compliant with CoaXPress standard"
969:"CoaXPress standard gets camera, frame grabber support"
946:
944:
190:, imaging various infrared bands, line scan imaging,
1589:
Hapgood, Fred (December 15, 2006 – January 1, 2007).
654:"Machine Vision Fundamentals, How to Make Robots See"
548:
Machine Vision for the Inspection of Natural Products
1225:
3-D Imaging: A practical Overview for Machine Vision
920:"Explore the Fundamentals of Machine Vision: Part 1"
337:: automated reading of text such as serial numbers
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682:
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256:Pixel counting: counts the number of light or dark
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1193:(4th ed.). Academic Press. pp. 410–411.
951:Critical Considerations for Embedded Vision Design
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1081:"Product Focus - Looking to the Future of Vision"
1022:Machine Vision - Theory Algorithms Practicalities
855:
853:
750:A Roadmap For Building A Machine Vision System
744:
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545:Graves, Mark & Bruce G. Batchelor (2003).
109:Imaging based automatic inspection and sorting
42:is the technology and methods used to provide
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1577:The Place for Deep Learning in Machine Vision
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343:: measurement of object dimensions (e.g. in
1404:"Introduction to Neural Net Machine Vision"
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89:discipline can be considered distinct from
58:discipline can be considered distinct from
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461:Machine Vision Algorithms and Applications
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1024:(2nd ed.). Harcourt & Company.
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242:: Combining of adjacent 2D or 3D images.
1740:Carbon nanotube field-effect transistor
1698:Applications of artificial intelligence
450:
2188:Electrical and electronics engineering
2173:Developmental and reproductive biology
1889:Differential technological development
715:LĂĽckenhaus, Maximilian (May 1, 2016).
1140:High Speed, Real-Time Machine Vision
7:
1859:Three-dimensional integrated circuit
893:Belbachir, Ahmed Nabil, ed. (2009).
1978:Future-oriented technology analysis
1718:Progress in artificial intelligence
1157:"3D Machine Vison Comes into Focus"
1155:Murray, Charles J (February 2012).
424:Feature detection (computer vision)
992:Wilson, Dave (November 12, 2012).
587:Holton, W. Conard (October 2010).
14:
2218:Genetics and evolutionary biology
624:Owen-Hill, Alex (July 21, 2016).
118:Methods and sequence of operation
2032:
1514:. Springer-Verlag. p. 191.
1489:. Springer-Verlag. p. 132.
1464:. Springer-Verlag. p. 125.
1436:. Springer-Verlag. p. 111.
1353:. Springer-Verlag. p. 108.
1079:Wilson, Andrew (December 2011).
953:by Dave Rice and Amber Thousand
691:"Introduction to Machine Vision"
2379:Applications of computer vision
2222:Cellular and molecular biology
1755:Fourth-generation optical discs
1378:. Springer-Verlag. p. 95.
1299:. Springer-Verlag. p. 96.
1274:. Springer-Verlag. p. 39.
967:Wilson, Andrew (May 31, 2011).
918:Dechow, David (February 2013).
762:Dechow, David (January 2009).
1:
2005:Technology in science fiction
1402:Turek, Fred D. (March 2007).
1109:Wilson, Andrew (April 2011).
764:"Integration: Making it Work"
335:Optical character recognition
290:Blob detection and extraction
1535:Hornberg, Alexander (2006).
860:Hornberg, Alexander (2006).
792:Hornberg, Alexander (2006).
652:Turek, Fred D. (June 2011).
395:Imaging based robot guidance
1049:Dinev, Petko (March 2008).
2400:
2323:Probability and statistics
2010:Technology readiness level
1946:Technological unemployment
1538:Handbook of Machine Vision
866:. Wiley-VCH. p. 429.
863:Handbook of Machine Vision
795:Handbook of Machine Vision
464:(2nd ed.). Weinheim:
103:glossary of machine vision
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2028:
1993:Technological singularity
1953:Technological convergence
1591:"Factories of the Future"
511:10.1007/978-3-662-47794-6
1765:Holographic data storage
717:"Machine Vision in IIoT"
132:digital image processing
2118:Artificial intelligence
1958:Technological evolution
1931:Exploratory engineering
1760:3D optical data storage
1693:Artificial intelligence
1142:CyberOptics, pages 1-38
1057:: 10–14. Archived from
770:: 16–20. Archived from
419:Machine vision glossary
279:: finding object edges
247:morphological filtering
124:acquisition of an image
2348:Structural engineering
2288:Mechanical engineering
2261:Western and South Asia
1968:Technology forecasting
1963:Technological paradigm
1936:Proactionary principle
1854:Software-defined radio
434:Vision processing unit
36:
2213:Environmental science
2098:Aerospace engineering
1894:Disruptive innovation
1654:Emerging technologies
1408:Vision Systems Design
1187:Davies, E.R. (2012).
1115:Vision Systems Design
1111:"The Infrared Choice"
1085:Vision Systems Design
1020:Davies, E.R. (1996).
998:Vision Systems Design
973:Vision Systems Design
924:Vision Systems Design
593:Vision Systems Design
188:hyperspectral imaging
184:multispectral imaging
22:
1941:Technological change
1884:Collingridge dilemma
1681:Ambient intelligence
1055:Vision & Sensors
768:Vision & Sensors
628:. Robotics Tomorrow.
429:Foreground detection
1998:Technology scouting
1973:Accelerating change
1703:Machine translation
836:. Springer-Verlag.
696:. Assembly Magazine
589:"By Any Other Name"
311:Pattern recognition
87:systems engineering
56:systems engineering
2015:Technology roadmap
1728:Speech recognition
1713:Mobile translation
1686:Internet of things
265:: Partitioning a
93:, a form of basic
37:
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2343:Scientific naming
2333:Quantum computing
2163:Computer hardware
2158:Clinical research
2153:Civil engineering
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1837:Optical computing
1552:978-3-527-40584-8
1031:978-0-12-206092-2
955:Photonics Spectra
904:978-1-4419-0952-7
873:978-3-527-40584-8
809:978-3-527-40584-8
562:978-1-85233-525-0
520:978-3-662-47793-9
475:978-3-527-41365-2
341:Gauging/Metrology
315:template matching
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2168:Computer science
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1983:Horizon scanning
1899:Ephemeralization
1815:Racetrack memory
1750:Extended reality
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1165:. Archived from
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304:machine learning
253:grayscale value.
245:Filtering (e.g.
214:Image processing
177:Gigabit Ethernet
99:industrial robot
95:computer science
83:image processing
64:computer science
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2251:Arabic toponyms
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2113:Architecture
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1924:Robot ethics
1723:Semantic Web
1707:
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286:using color.
263:Segmentation
240:Registration
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179:interfaces.
152:smart camera
149:
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62:, a form of
39:
38:
2313:Ornithology
2298:Meteorology
2283:Mathematics
2273:Ichthyology
2108:Archaeology
2103:Agriculture
2087:engineering
1988:Moore's law
1919:Neuroethics
1914:Cyberethics
1735:Atomtronics
1162:Design News
353:millimeters
331:" reading
325:Data Matrix
161:Camera Link
2368:Categories
2328:Psychiatry
2237:Geography
2208:Entomology
1879:Automation
1612:2010-10-28
1558:2010-11-05
1418:2013-03-05
1206:2012-05-13
1173:2012-05-12
1125:2013-03-05
1121:(4): 20–23
1095:2013-03-05
1065:2012-05-12
1003:2013-03-05
978:2012-11-28
934:2013-03-05
930:(2): 14–15
879:2010-11-05
815:2010-11-05
778:2012-05-12
752:Pages 1-35
700:9 February
670:2011-11-29
666:(6): 60–62
610:2013-03-05
568:2010-11-02
526:2016-10-11
501:. Berlin:
481:2018-01-30
445:References
329:2D barcode
313:including
196:frame rate
192:3D imaging
77:Definition
2183:Economics
2148:Chemistry
2123:Astronomy
1909:Bioethics
1795:Millipede
1607:0894-9301
1601:(6): 46.
1543:Wiley-VCH
800:Wiley-VCH
605:1089-3709
466:Wiley-VCH
381:The term
236:Stitching
204:depth map
165:CoaXPress
138:Equipment
48:automatic
25:Automatix
2353:Virology
2338:Robotics
2303:Mycology
2293:Medicine
2138:Calculus
1830:UltraRAM
553:Springer
503:Springer
413:See also
284:features
271:segments
169:FireWire
128:software
71:analysis
2318:Physics
2268:Geology
2178:Ecology
2128:Biology
2083:science
1776:Memory
364:Outputs
321:Barcode
146:Imaging
46:-based
44:imaging
2133:Botany
1904:Ethics
1872:Topics
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349:inches
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1785:ECRAM
1780:CBRAM
1772:GPGPU
694:(PDF)
327:and "
29:Omron
2085:and
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1842:RFID
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1810:PRAM
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1800:MRAM
1790:FRAM
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351:or
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175:or
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