SlideShare a Scribd company logo
1 of 19
以動差(moment)為基礎
之智能影像量測技術
電光所 生產線檢測技術部
2014.10.15
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 2
Outline
• Introduction of AOI
• Pattern Matching
• Results and Discussion
• HAWK AOI System
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 3
Automatic Optical Inspection
Lighting
Capturing
Processing
Data
Collection AOI
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 4
Pattern Matching
• Automatic Measurement
• User friendly
– To place the object at arbitrary position and angle
• How to do?
– Brute force
• 2560 x 1920
• Pattern:
– 1040 x 984 Pattern
Captured Image
1520 x 936 x 360 = 512179200 !!
(8536.32 min. = 142.27 hrs. = 5.93 days)
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 5
Hierarchical Approach
Original Image
1/4 size Image
1/16 size Image
1/64 size Image
Pattern
Captured Image
(40 x 30)
(160 x 120)
(640 x 480)
(2560 x 1920)
40 x 30 x 360 = 432000
20 x 20 x 20 = 8000
20 x 20 x 20 = 8000
20 x 20 x 20 = 8000
456000 !!
(7.6 min. << 8536 min.)
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 6
Hierarchical Approach
But, how about this object?
Original Image
1/4 size Image
1/16 size Image
1/64 size Image
Pattern
Cannot detect the orientation!!
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 7
動差(moment)智能影像量測技術
7
• Moment of an image • Central moments
• Symmetric Axis
with a,b, and c respectively
being the 2nd central moments μpq
defined as: pq=20, 02 and 11
𝑀𝑖𝑗 = 𝑥𝑖 𝑦𝑗 𝐼(𝑥, 𝑦)
𝑦𝑥
𝜇 𝑝𝑞 = 𝑥 − 𝑥 𝑝 𝑦 − 𝑦 𝑞 𝑓(𝑥, 𝑦)
𝑦𝑥
sin 2𝜃 =
±𝑏
𝑏2 + (𝑎 − 𝑐)2
References:
• Image analysis by moments, PhD thesis, S.X. Liao
http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?highlight=moments#moments
• “Visual Pattern Recognition by Moment Invariants", Ku, 1962
http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OWENS/LECT2/node3.html
Orientation of group of points
θ
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 8
Verifications of Rotation Angle
0.99º1º 3º
1º1º
1º
3º
1.01º
3.00º
3.00º
3º 3.00º
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 9
Pattern Matching
𝜇 𝑝𝑞 = 𝑥 − 𝑥 𝑝 𝑦 − 𝑦 𝑞 𝑓(𝑥, 𝑦)
𝑦𝑥
sin 2𝜃 =
±𝑏
𝑏2 + (𝑎 − 𝑐)2
Computation Time:
276.945 ms!!
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 10
Experimental Results _1
Pattern Image
Matching Result_1
Matching Result_2
Matching Result_3
Process Time: 320 ms
(image size: 3840 x 2748)
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 11
Experimental Results _2
Pattern Image
Matching Result_1
Matching Result_2
Matching Result_3
Process Time: 297 ms
(image size: 3840 x 2748)
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 12
Experimental Results _3
Pattern Image
Matching Result_1
Matching Result_2
Matching Result_3
Process Time: 304 ms
(image size: 3840 x 2748)
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 13
Outline
• Introduction of AOI
• Pattern Matching
• Results and Discussion
• HAWK AOI System
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 14
Performance of HAWK AOI System
• Accuracy
– Target: 光學標準片
• 國家標準實驗室送校
– 測試程序
• 每個量測項目測試15筆資料
1
2
3
4
5 圓徑 實測值
6 6.00025
10 9.99676
14 13.99846
18 17.99488
22 21.99642
24台量測誤差皆小於 3 m !!
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 15
Performance of HAWK AOI System
• Repeatability
– How to setup the standard deviation (1)?
1σ=1.5 μm GRR=7.5%
(USL-LSL= 20μm)
Ca=5% Cp=2.22 Cpk=2.1
A級 A+級 A+級
廠商最嚴謹需求:0.02 mm  0.04 mm
0.04  10% = 0.004 mm = 4 m
必須滿足99% (5.15) 的測試範圍
4 / 5.15 = 0.77 m
Excellent: 0.77 m
Good: 1.54 m
Normal: 2.21 m
Normal
2.21 m
Good
1.54 m
Excellent
0.77 m
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 16
• GR&R Analysis
– AIAG standards
• 10 parts, 3 appraisers,
and 3 trials
% Total Tolerance
Percent Equipment Variation
%EV = 100 [ EV / Total ]
= 100 [0.0185 / 0.2 ]
= 9.25 %
Percent Appraiser Variation
%AV = 100 [ AV / Total ]
= 100 [0.0052 / 0.2 ]
= 2.6 %
Percent Gage Repeatability & Reproducibility Variation
%GRR = 100 [ GRR / Total ]
= 100 [0.0192 / 0.2 ]
= 9.6 %
8.177 mm
Performance of HAWK AOI System
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 17
生產線上精密量測機
• 高量測重現精度 ±3μ(2σ)
• 適用於CNC產線的IPQC全檢系統
• 連結製程統計管理系統 (SPC)
• 授權國內自動化業者生產,設備完全國產化。
Aerospace
fasteners
Rough machining
CNC machining center
intermediate machining
Precision Image Insepction + SPC
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 18
Conclusion
• Pattern Matching
– Image Moment
• Computation Time
– 8536 min  7.6 mm  276.9 ms
• Measure Performance
– Accuracy < 3 m
– Stability (1 ) < 3 m
工業技術研究院內部資訊,禁止複製、轉載、外流,不再使用請銷毀│ ITRI proprietary documents, do not copy or distribute, please destroy after use 19
專心追求卓越,成功自然就會跟著你!
Chase Excellence, Success will follow.

More Related Content

Similar to 以動差為基礎之智能影像量測技術_電光所-賴岳益博士

Mining Assumptions for Software Components using Machine Learning
Mining Assumptions for Software Components using Machine LearningMining Assumptions for Software Components using Machine Learning
Mining Assumptions for Software Components using Machine Learning
Lionel Briand
 
Intel Atom Processor Pre-Silicon Verification Experience
Intel Atom Processor Pre-Silicon Verification ExperienceIntel Atom Processor Pre-Silicon Verification Experience
Intel Atom Processor Pre-Silicon Verification Experience
DVClub
 
Opti struct tips_and_tricks_atc
Opti struct tips_and_tricks_atcOpti struct tips_and_tricks_atc
Opti struct tips_and_tricks_atc
Altair
 
Workmanship Essentials
Workmanship EssentialsWorkmanship Essentials
Workmanship Essentials
Ernie Marks
 
AJAY NANOCHIP RESUME
AJAY NANOCHIP RESUMEAJAY NANOCHIP RESUME
AJAY NANOCHIP RESUME
AJAY ABRAHAM
 

Similar to 以動差為基礎之智能影像量測技術_電光所-賴岳益博士 (20)

極紫外線散射儀於先進製程檢測應用
極紫外線散射儀於先進製程檢測應用極紫外線散射儀於先進製程檢測應用
極紫外線散射儀於先進製程檢測應用
 
Challenges in Assessing Single Event Upset Impact on Processor Systems
Challenges in Assessing Single Event Upset Impact on Processor SystemsChallenges in Assessing Single Event Upset Impact on Processor Systems
Challenges in Assessing Single Event Upset Impact on Processor Systems
 
Mining Assumptions for Software Components using Machine Learning
Mining Assumptions for Software Components using Machine LearningMining Assumptions for Software Components using Machine Learning
Mining Assumptions for Software Components using Machine Learning
 
kao_resume 2016
kao_resume 2016kao_resume 2016
kao_resume 2016
 
ADVEInc "Meet the Experts Forum 25February2010 - Electronics & IC Design Serv...
ADVEInc "Meet the Experts Forum 25February2010 - Electronics & IC Design Serv...ADVEInc "Meet the Experts Forum 25February2010 - Electronics & IC Design Serv...
ADVEInc "Meet the Experts Forum 25February2010 - Electronics & IC Design Serv...
 
JosephAnthonyEAlvarez_CV_2016
JosephAnthonyEAlvarez_CV_2016JosephAnthonyEAlvarez_CV_2016
JosephAnthonyEAlvarez_CV_2016
 
Industrial use of formal methods
Industrial use of formal methodsIndustrial use of formal methods
Industrial use of formal methods
 
Intel Atom Processor Pre-Silicon Verification Experience
Intel Atom Processor Pre-Silicon Verification ExperienceIntel Atom Processor Pre-Silicon Verification Experience
Intel Atom Processor Pre-Silicon Verification Experience
 
Module5 Testing and Verification.pdf
Module5 Testing and Verification.pdfModule5 Testing and Verification.pdf
Module5 Testing and Verification.pdf
 
U10_ReverseEngineering.pptx
U10_ReverseEngineering.pptxU10_ReverseEngineering.pptx
U10_ReverseEngineering.pptx
 
6.3a.ppt
6.3a.ppt6.3a.ppt
6.3a.ppt
 
Capstone Project
Capstone ProjectCapstone Project
Capstone Project
 
CNC Mill
CNC MillCNC Mill
CNC Mill
 
TRECVID 2016 : Concept Localization
TRECVID 2016 : Concept LocalizationTRECVID 2016 : Concept Localization
TRECVID 2016 : Concept Localization
 
A Computer Vision Application for In Vitro Diagnostics Devices
A Computer Vision Application for In Vitro Diagnostics DevicesA Computer Vision Application for In Vitro Diagnostics Devices
A Computer Vision Application for In Vitro Diagnostics Devices
 
Opti struct tips_and_tricks_atc
Opti struct tips_and_tricks_atcOpti struct tips_and_tricks_atc
Opti struct tips_and_tricks_atc
 
Automated Defect Classifier for PCBs using Raspberry Pi
Automated Defect Classifier for PCBs using Raspberry PiAutomated Defect Classifier for PCBs using Raspberry Pi
Automated Defect Classifier for PCBs using Raspberry Pi
 
Workmanship Essentials
Workmanship EssentialsWorkmanship Essentials
Workmanship Essentials
 
VLSI testing and analysis
VLSI testing and analysisVLSI testing and analysis
VLSI testing and analysis
 
AJAY NANOCHIP RESUME
AJAY NANOCHIP RESUMEAJAY NANOCHIP RESUME
AJAY NANOCHIP RESUME
 

More from CHENHuiMei

More from CHENHuiMei (20)

小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
 
QIF對AOI設備業之衝擊與機會
QIF對AOI設備業之衝擊與機會QIF對AOI設備業之衝擊與機會
QIF對AOI設備業之衝擊與機會
 
產研融合推手-台大AOI設備研發聯盟_台大陳亮嘉
產研融合推手-台大AOI設備研發聯盟_台大陳亮嘉產研融合推手-台大AOI設備研發聯盟_台大陳亮嘉
產研融合推手-台大AOI設備研發聯盟_台大陳亮嘉
 
基於少樣本深度學習之橡膠墊片檢測系統
基於少樣本深度學習之橡膠墊片檢測系統基於少樣本深度學習之橡膠墊片檢測系統
基於少樣本深度學習之橡膠墊片檢測系統
 
AOI智慧升級─AI訓練師在地養成計畫_台灣人工智慧學校
AOI智慧升級─AI訓練師在地養成計畫_台灣人工智慧學校AOI智慧升級─AI訓練師在地養成計畫_台灣人工智慧學校
AOI智慧升級─AI訓練師在地養成計畫_台灣人工智慧學校
 
使用人工智慧檢測三維錫球瑕疵_台大傅楸善
使用人工智慧檢測三維錫球瑕疵_台大傅楸善使用人工智慧檢測三維錫球瑕疵_台大傅楸善
使用人工智慧檢測三維錫球瑕疵_台大傅楸善
 
IIoT發展趨勢及設備業者因應之_微軟葉怡君
IIoT發展趨勢及設備業者因應之_微軟葉怡君IIoT發展趨勢及設備業者因應之_微軟葉怡君
IIoT發展趨勢及設備業者因應之_微軟葉怡君
 
精密機械的空間軌跡精度光學檢測法_台大范光照
精密機械的空間軌跡精度光學檢測法_台大范光照精密機械的空間軌跡精度光學檢測法_台大范光照
精密機械的空間軌跡精度光學檢測法_台大范光照
 
Report
ReportReport
Report
 
Deep learning
Deep learningDeep learning
Deep learning
 
When AOI meets AI
When AOI meets AIWhen AOI meets AI
When AOI meets AI
 
2018AOI論壇_基於生成對抗網路之非監督式AOI技術_工研院蔡雅惠
2018AOI論壇_基於生成對抗網路之非監督式AOI技術_工研院蔡雅惠2018AOI論壇_基於生成對抗網路之非監督式AOI技術_工研院蔡雅惠
2018AOI論壇_基於生成對抗網路之非監督式AOI技術_工研院蔡雅惠
 
2018AOIEA論壇Keynote_眺望趨勢 量測設備未來10年發展重點_致茂曾一士
2018AOIEA論壇Keynote_眺望趨勢 量測設備未來10年發展重點_致茂曾一士2018AOIEA論壇Keynote_眺望趨勢 量測設備未來10年發展重點_致茂曾一士
2018AOIEA論壇Keynote_眺望趨勢 量測設備未來10年發展重點_致茂曾一士
 
2018AOI論壇Keynote_AI入魂製造領域現況與趨勢_工研院熊治民
2018AOI論壇Keynote_AI入魂製造領域現況與趨勢_工研院熊治民2018AOI論壇Keynote_AI入魂製造領域現況與趨勢_工研院熊治民
2018AOI論壇Keynote_AI入魂製造領域現況與趨勢_工研院熊治民
 
2018AOI論壇_AOI and IoT產線應用_工研院周森益
2018AOI論壇_AOI and IoT產線應用_工研院周森益2018AOI論壇_AOI and IoT產線應用_工研院周森益
2018AOI論壇_AOI and IoT產線應用_工研院周森益
 
2018AOI論壇_AOI參與整廠協作之實務建議_達明機器人黃鐘賢
2018AOI論壇_AOI參與整廠協作之實務建議_達明機器人黃鐘賢2018AOI論壇_AOI參與整廠協作之實務建議_達明機器人黃鐘賢
2018AOI論壇_AOI參與整廠協作之實務建議_達明機器人黃鐘賢
 
2018AOI論壇_深度學習在電腦視覺應用上的疑問_中央大學曾定章
2018AOI論壇_深度學習在電腦視覺應用上的疑問_中央大學曾定章2018AOI論壇_深度學習在電腦視覺應用上的疑問_中央大學曾定章
2018AOI論壇_深度學習在電腦視覺應用上的疑問_中央大學曾定章
 
2018AOI論壇_深度學習於表面瑕疪檢測_元智大學蔡篤銘
2018AOI論壇_深度學習於表面瑕疪檢測_元智大學蔡篤銘2018AOI論壇_深度學習於表面瑕疪檢測_元智大學蔡篤銘
2018AOI論壇_深度學習於表面瑕疪檢測_元智大學蔡篤銘
 
2018AOI論壇_時機已到 AOI導入邊緣運算_SAS林育宏
2018AOI論壇_時機已到 AOI導入邊緣運算_SAS林育宏2018AOI論壇_時機已到 AOI導入邊緣運算_SAS林育宏
2018AOI論壇_時機已到 AOI導入邊緣運算_SAS林育宏
 
2018AOI論壇_如何導入深度學習來提升工業瑕疵檢測技術_工研院賴璟皓
2018AOI論壇_如何導入深度學習來提升工業瑕疵檢測技術_工研院賴璟皓2018AOI論壇_如何導入深度學習來提升工業瑕疵檢測技術_工研院賴璟皓
2018AOI論壇_如何導入深度學習來提升工業瑕疵檢測技術_工研院賴璟皓
 

Recently uploaded

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Recently uploaded (20)

الأمن السيبراني - ما لا يسع للمستخدم جهله
الأمن السيبراني - ما لا يسع للمستخدم جهلهالأمن السيبراني - ما لا يسع للمستخدم جهله
الأمن السيبراني - ما لا يسع للمستخدم جهله
 
Decarbonising Commercial Real Estate: The Role of Operational Performance
Decarbonising Commercial Real Estate: The Role of Operational PerformanceDecarbonising Commercial Real Estate: The Role of Operational Performance
Decarbonising Commercial Real Estate: The Role of Operational Performance
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
Simplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxSimplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptx
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptx
 
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps Productivity
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cf
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Design and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data ScienceDesign and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data Science
 
Quantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation ComputingQuantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation Computing
 
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 

以動差為基礎之智能影像量測技術_電光所-賴岳益博士