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AOI
Albert Y. C. Chen, Ph.D.
Vice President, R&D
Viscovery
Albert Y. C. Chen, Ph.D.
•
2017– Viscovery
2016–2017 Viscovery
2015–2015 Nervve Technologies
2013–2014 Tandent Vision Science
2011–2012 GE Global Research
•
Computer Science
Computer Science
Computer Science
• AI 2017/07/26
• CV ML 2016/07/21
• GPU
2017/06/27
• GMIC
2017/04
• AI Mix Taiwan 2017/03
• MIT 2017/01
• 2016/12
AOI
•
•
•
AOI
Cognex AOI
Cognex AOI
• Final inspection cells
• Robot guidance and
checking orientation of
components
• Packaging Inspection
• Medical vial inspection
• Food pack checks
• Verifying engineered
components[5]
• Wafer Dicing
• Reading of Serial
Numbers
• Inspection of Saw
Blades
• Inspection of Ball Grid
Arrays (BGAs)
• Surface Inspection
• Measuring of Spark
Plugs
• Molding Flash Detection
• Inspection of Punched
Sheets
• 3D Plane
Reconstruction with
Stereo
• Pose Verification of
Resistors
• Classification of Non-
Woven Fabrics
AOI
• Automated Train
Examiner (ATEx)
Systems
• Automatic PCB
inspection
• Wood quality
inspection
• Final inspection of
sub-assemblies
• Engine part inspection
• Label inspection on
products
• Checking medical
devices for defects
•
• 25
• 8 6
• 6 8
• 6 6
• 1703
X. Gilbert / arXiv 2015
X. Gilbert / arXiv 2015
X. Gilbert / arXiv 2015
E. Protopapadakis et al. / ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial
Information Sciences, Volume III-5, 2016
E. Protopapadakis et al. / ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial
Information Sciences, Volume III-5, 2016
X
T W Rogers et al. / SPIE Defense and Security 2017 (to appear)
X
T W Rogers et al. / SPIE Defense and Security 2017 (to appear)
X
T W Rogers et al. / SPIE Defense and Security 2017 (to appear)
x
y
Cartesiancoordinates
r
θ
Polarcoordinates
gure1.1:Exampleofdifferentrepresentations:supposewewantto
tegoriesofdatabydrawingalinebetweentheminascatterplot.Inthep
representsomedatausingCartesiancoordinates,andthetaskisimpossib
theright,werepresentthedatawithpolarcoordinatesandthetaskbeco
vewithaverticalline.FigureproducedincollaborationwithDavidW
Onesolutiontothisproblemistousemachinelearningtodisco
emappingfromrepresentationtooutputbutalsotherepresen
hisapproachisknownasrepresentationlearning.Learnedrep
tenresultinmuchbetterperformancethancanbeobtainedwithh
presentations.TheyalsoallowAIsystemstorapidlyadapttonew
nimalhumanintervention.Arepresentationlearningalgorithmc
odsetoffeaturesforasimpletaskinminutes,oracomplextas
onths.Manuallydesigningfeaturesforacomplextaskrequiresa
mantimeandeffort;itcantakedecadesforanentirecommunityo
Thequintessentialexampleofarepresentationlearningalgorith
encoder.Anautoencoderisthecombinationofanencoderf
nvertstheinputdataintoadifferentrepresentation,andadeco
• 28x28
• 1980 MNIST
>1170
•
•
AI (1970-1980, 1990-2000)
•
( )zσ+
( )zσ+
( )zσ+
( )zσ+
1970
• Convolutional Neural Network
1970
1970
Large
Small
1x
2x
……
Nx
……
……
……
……
……
……
……
y1
y2
yM
 
 
 
 
 
1970
w1
w2
Clipping
[Razvan Pascanu, ICML’13]
1970
• Mini-batch
• Adaptive
Learning Rate
• Dropout, Batch-
normalization
minibatchminibatch
1 epoch
1970
( )
•
( )
•
•
•
•
( )
•
•
•
•
•
D Weimer et al. 2017
X
Funding Li et al. / IEEE Tran Automation Science and Engineer 2017 (to appear)
Johannes Günther et al. / Procedia Technology 15 (2014) 474 – 483
Johannes Günther et al. / Procedia Technology 15 (2014) 474 – 483
S. N. Lim et al. / GE Global Research
S. N. Lim et al. / GE Global Research
S. N. Lim et al. / GE Global Research
AI
•
•
•
AI
Viscovery
Optical Character
Recognition
Offline
Recognition2010
2013
2014
Product Recognition
2015
Video Content related
Advertisements
2017
Wearable
Devices
Video Content
Discovery & Interaction
2016
AI
Viscovery
• 2014 800
AI
Viscovery
•
AI
Viscovery
•
Thank You!
albert@viscovery.com
http://slideshare.net/albertycchen

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