20.7 Industrial Applications

Tsuji, S.,
Future Directions of Industrial Applications,
ICPR78(1144-1145). BibRef 7800

Baird, M.L.,
Future Directions of Industrial Applications of Pattern Recognition,
ICPR78(1146). BibRef 7800

Uno, T.,
Future Directions of Industrial Applications,
ICPR78(1147). BibRef 7800

Weaver, J.A.,
Some Thoughts on Future Directions of Industrial Applications,
ICPR78(1148-1149). BibRef 7800

Thompson, W.B.,
Machine Perception for Industrial Applications,
Computer(13), No. 5, May 1980, pp. 7-8. Introduction to the special issue. BibRef 8005

Kruger, R.P., and Thompson, W.B.,
A Technical and Economic Assessment of Computer Vision for Industrial Inspection and Robotic Assembly,
PIEEE(69), No. 12, December 1981, 1524-1538. BibRef 8112

Rosenfeld, A.,
Machine Vision for Industry: Tasks, Tools, and Techniques,
IVC(3), No. 3, August 1985, pp. 122-135.
Elsevier DOI BibRef 8508

Mundy, J.L.,
Industrial Machine Vision -- Is It Practical?,
MVAAS88(xx-yy). Life cycle of applied vision systems. BibRef 8800

Wiitanen, W.,
A Perspective on Machine Vision at General Motors,
MVAAS88(xx-yy). BibRef 8800

Shirai, Y.,
Robot Vision,
FGCS(1), No. 5, September 1985, pp. 325-352. Survey, Industrial Applications. Industrial Vision, Survey. A survey of computer techniques used in industrial applications especially in Japan. Noticeably simple techniques that work. BibRef 8509

Clune, E., Crisman, J.D., Klinker, G.J., and Webb, J.A.,
Implementation and Performance of a Complex Vision System on a Systolic Array Machine,
FGCS(4), No. 1, August 1988, pp. 13-30. Developed from the FIDO system. BibRef 8808

Gonzalez, R.C., and Safabakhsh, R.,
Computer Vision Techniques for Industrial Applications and Robot Control,
Computer(15), No. 12, December 1982, pp. 17-33. BibRef 8212

Fu, K.S.,
Pattern Recognition for Automatic Visual Inspection,
Computer(15), No. 12, December 1982, pp. 34-41. BibRef 8212

Jarvis, J.F.,
Research Directions in Industrial Machine Vision: A Workshop Summary,
Computer(15), No. 12, December 1982, pp. 55-61. BibRef 8212

Wallace, A.M.[Andrew M.],
Greyscale Image Processing for Industrial Applications,
IVC(1), No. 4, November 1983, pp. 178-188.
Elsevier DOI BibRef 8311

Hudson, D.L.[David L.],
Practical Solution Using a New Approach to Robot Vision,
IVC(1), No. 4, November 1983, pp. 234-240.
Elsevier DOI for keyboard manufacturing. BibRef 8311

Noble, J.A.,
From Inspection to Process Understanding and Monitoring: A View on Computer Vision in Manufacturing,
IVC(13), No. 3, April 1995, pp. 197-214.
Elsevier DOI BibRef 9504

Chen, C.H.,
Pattern Recognition in Nondestructive Evaluation of Materials,
HPRCV97(Chapter III:1). (Univ. Massachusetts Dartmouth) BibRef 9700

Batchelor, B.G., Whelan, P.F.,
Intelligent Vision Systems for Industry,
Springer-Verlag1997, ISBN 3-540-19969-1.
WWW Link. BibRef 9700

Murino, V.[Vittorio], Trucco, A.[Andrea],
Underwater Computer Vision and Pattern Recognition,
CVIU(79), No. 1, July 2000, pp. 1-3.
DOI Link 0006
Intro to the section. BibRef

Cheriet, M., Yang, Y.H.,
Special Issue: Vision Interface '98 - Real World Applications of Computer Vision - Preface,
PRAI(13), No. 5, August 1999, pp. 589. 0005
BibRef

Asimopoulos, N.[Nikos], Nadler, M.[Morton],
Non-contact velocity compensation system for handheld scanners,
PR(35), No. 2, February 2002, pp. 465-472.
Elsevier DOI 0201
BibRef

Malamas, E.N.[Elias N.], Petrakis, E.G.M.[Euripides G. M.], Zervakis, M.E.[Michalis E.], Petit, L.[Laurent], Legat, J.D.[Jean-Didier],
A survey on industrial vision systems, applications and tools,
IVC(21), No. 2, February 2003, pp. 171-188.
Elsevier DOI 0301
Survey, Industrial Applications. BibRef

Vila, J.[Joan], Calpe, J.[Javier], Pla, F.[Filiberto], Gómez, L.[Luis], Connell, J.[Joseph], Marchant, J.[John], Calleja, J.[Javier], Mulqueen, M.[Michael], Muñoz, J.[Jordi], Klaren, A.C.[Arnoud C.], Team, T.S.[The SmartSpectra],
SmartSpectra: Applying multispectral imaging to industrial environments,
RealTimeImg(11), No. 2, April 2005, pp. 85-98.
Elsevier DOI 0506
BibRef

Paclík, P.[Pavel], Leitner, R.[Raimund], Duin, R.P.W.[Robert P. W.],
A study on design of object sorting algorithms in the industrial application using hyperspectral imaging,
RealTimeIP(1), No. 2, December 2006, pp. 101-108.
Springer DOI 0001
BibRef

Billingsley, J.[John], Bradbeer, R.[Robin], (Eds.)
Mechatronics and Machine Vision in Practice,
Springer2008, ISBN: 978-3-540-74026-1.
WWW Link. Survey, Robotics. BibRef 0800

Gan, Z.X.[Zhong-Xue], Tang, Q.[Qing],
Visual Sensing and its Applications: Integration of Laser Sensors to Industrial Robots,
Springer2011. ISBN: 978-3-642-18286-0.
WWW Link. 1109
BibRef

Liu, C., Qiao , H., Zhang, B.,
Stable Sensorless Localization of 3-D Objects,
SMC-C(41), No. 6, November 2011, pp. 923-941.
IEEE DOI 1110
In manfacturing. 2-D is easier to analyze. BibRef

Fenn, S.[Shannon], Mendes, A.[Alexandre], Budden, D.M.[David M.],
Addressing the non-functional requirements of computer vision systems: a case study,
MVA(27), No. 1, January 2016, pp. 77-86.
WWW Link. 1601
BibRef

Hua, X.[Xin], Zhang, C.H.[Chun-Hua], Wei, J.[Jinda], Hu, X.J.[Xing-Jun], Wei, H.L.[Hong-Liang],
Wind turbine bionic blade design and performance analysis,
JVCIR(60), 2019, pp. 258-265.
Elsevier DOI 1903
Numerical simulation, Source of renewable energy, Wind turbine blade BibRef

Scharf, D.[Dietmar], Viet, B.L.[Bach Le], Le, T.B.H.[Thi Bich Hoa], Rechenberg, J.[Janine], Tschierschke, S.[Stefan], Vogl, E.[Ernst], Vandone, A.[Ambra], Giardini, M.[Mattia],
Hardware Accelerated Image Processing on an Fpga-soc Based Vision System for Closed Loop Monitoring and Additive Manufacturing Process Control,
CVS19(3-12).
Springer DOI 1912
BibRef

Stenz, U.[Ulrich], Hartmann, J.[Jens], Paffenholz, J.A.[Jens-André], Neumann, I.[Ingo],
High-Precision 3D Object Capturing with Static and Kinematic Terrestrial Laser Scanning in Industrial Applications: Approaches of Quality Assessment,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Yang, Y.K.[Yi-Kun], Jiao, S.J.[Sheng-Jie], Li, J.B.[Jia-Bo],
Vision-based optimization of the generalized predictive active disturbance rejection controller,
JVCIR(71), 2020, pp. 102728.
Elsevier DOI 2009
Mixing and spreading equipment for MOH material, Batching system, Active disturbance rejection control, Adaptive genetic algorithm BibRef

Xie, Y.L.[Yi-Lin], Wang, Q.[Qing], Yao, L.B.[Lian-Bi], Meng, X.L.[Xiao-Lin], Yang, Y.S.[Yu-Song],
Integrated Multi-Sensor Real Time Pile Positioning Model and Its Application for Sea Piling,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Manso-Callejo, M.Á.[Miguel-Ángel], Cira, C.I.[Calimanut-Ionut], Alcarria, R.[Ramón], Arranz-Justel, J.J.[José-Juan],
Optimizing the Recognition and Feature Extraction of Wind Turbines through Hybrid Semantic Segmentation Architectures,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Wan, J., Li, X., Dai, H.N., Kusiak, A., Martínez-García, M., Li, D.,
Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges,
PIEEE(109), No. 4, April 2021, pp. 377-398.
IEEE DOI 2104
Artificial intelligence, Manufacturing, Smart manufacturing, Adaptation models, Production facilities, Heuristic algorithms, software-defined network BibRef

Ji, S.H.[Sang-Hoon], Lee, S.[Sukhan], Yoo, S.J.[Su-Jeong], Suh, I.[Ilhong], Kwon, I.[Inso], Park, F.C.[Frank C.], Lee, S.Y.[Sangh-Young], Kim, H.[Hongseok],
Learning-Based Automation of Robotic Assembly for Smart Manufacturing,
PIEEE(109), No. 4, April 2021, pp. 423-440.
IEEE DOI 2104
Robotic assembly, Uncertainty, Prototypes, Planning, Task analysis, Learning systems, Smart manufacturing, Robotic assembly, smart manufacturing BibRef

Xu, L.[Liang], Song, Y.K.[Yong-Kang], Zhang, W.[Weishan], An, Y.Y.[Yun-Yun], Wang, Y.[Ye], Ning, H.S.[Huan-Sheng],
An efficient foreign objects detection network for power substation,
IVC(109), 2021, pp. 104159.
Elsevier DOI 2105
Power substation, Deep learning, Foreign objects detection, FODN4PS BibRef

Sima, R.H.[Rui-Heng], Hao, X.P.[Xiao-Peng], Song, J.[Jian], Qi, H.[Hong], Yuan, Z.D.[Zun-Dong], Ding, L.[Lei], Duan, Y.N.[Yu-Ning],
Research on the Temperature Transfer Relationship Between Miniature Fixed-Point and Blackbody for On-Orbit Infrared Remote Sensor Calibration,
GeoRS(59), No. 7, July 2021, pp. 6266-6276.
IEEE DOI 2106
Temperature measurement, Calibration, Temperature sensors, Heating systems, Phase change materials, Remote sensing, temperature transfer relationship BibRef

Hu, D.L.[Dun-Li], Zhang, Y.T.[Yu-Ting], Li, X.F.[Xu-Feng], Zhang, X.P.[Xiao-Ping],
Detection of material on a tray in automatic assembly line based on convolutional neural network,
IET-IPR(15), No. 13, 2021, pp. 3400-3409.
DOI Link 2110
BibRef

Yang, X.[Xue], Sun, S.M.[Shi-Ming], Chen, W.[Wei], Liu, J.[Jing],
Underwater bubble plume image generative model based on noise prior and multi conditional labels,
IVC(119), 2022, pp. 104373.
Elsevier DOI 2202
Risks of underwater gas pipelines. Underwater bubble plumes, Noise prior, VAEs, Multi conditional label, Generative model, Discriminative model BibRef

Zhou, L.F.[Long-Fei], Zhang, L.[Lin], Konz, N.[Nicholas],
Computer Vision Techniques in Manufacturing,
SMCS(53), No. 1, January 2023, pp. 105-117.
IEEE DOI 2301
Image edge detection, Image segmentation, Task analysis, Robot sensing systems, Sensors, Feature detection, Assembly, survey BibRef

Fisher, M.[Mark], French, G.[Geoffrey], Gorpincenko, A.[Artjoms], Holah, H.[Helen], Clayton, L.[Lauren], Skirrow, R.[Rebecca], Mackiewicz, M.[Michal],
Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels,
IET-IPR(17), No. 2, 2023, pp. 349-361.
DOI Link 2302
BibRef

Tang, T.W.[Ta-Wei], Hsu, H.[Hakiem], Li, K.M.[Kuan-Ming],
Industrial anomaly detection with multiscale autoencoder and deep feature extractor-based neural network,
IET-IPR(17), No. 6, 2023, pp. 1752-1761.
DOI Link 2305
image classification, image recognition, inspection, unsupervised learning BibRef

Zhang, Y.[Yang], Cheng, L.[Le], Peng, Y.T.[Yu-Ting], Xu, C.M.[Cheng-Ming], Fu, Y.W.[Yan-Wei], Wu, B.[Bo], Sun, G.D.[Guo-Dong],
Faster OreFSDet: A lightweight and effective few-shot object detector for ore images,
PR(141), 2023, pp. 109664.
Elsevier DOI 2306
particle size of the ore in crushing operations. Ore images, Few-shot object detection, Real-time, Light-weight BibRef

Li, T.Z.[Tian-Zhu], Ma, C.H.[Cai-Hong], Lv, Y.Z.[Yong-Ze], Liao, R.[Ruilin], Yang, J.[Jin], Liu, J.B.[Jian-Bo],
An Approach to Large-Scale Cement Plant Detection Using Multisource Remote Sensing Imagery,
RS(16), No. 4, 2024, pp. 729.
DOI Link 2402
BibRef

Ni, Z.M.[Zi-Ming], Chen, X.Z.[Xian-Zhong], Hou, Q.W.[Qing-Wen], Zhang, J.[Jie],
Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors,
RS(16), No. 9, 2024, pp. 1509.
DOI Link 2405
Blast furnace monitoring. BibRef

Qian, Y.J.[Yan-Jun], Hulsizer, J.[Joel], Mou, M.Y.[Ming-Yao], Zambrano, C.V.[Consuelo Vega], Smith, E.[Ema], Jiang, M.[Mo],
Automatic measurement of slug flow processes from in-line videos,
IET-IPR(18), No. 8, 2024, pp. 2038-2052.
DOI Link 2406
feature extraction, length measurement, object detection, uncertainty handling, video retrieval BibRef

Kadeethum, T.[Teeratorn], Downs, C.[Christine],
Harnessing Machine Learning and Data Fusion for Accurate Undocumented Well Identification in Satellite Images,
RS(16), No. 12, 2024, pp. 2116.
DOI Link 2406
Oil and gas wells. Orphan, abandoned. BibRef

Xiang, D.[Dong], Chen, S.[Sizhuo], Liu, M.[Mengna], Shi, F.[Fan], Cheng, X.[Xu],
Multilevel Signal Decomposition Layer-Specific Residual Network for Blade Icing Prediction,
SPLetters(31), 2024, pp. 2160-2164.
IEEE DOI 2409
Feature extraction, Blades, Wind turbines, Data models, Data mining, Noise, Noise measurement, Blade icing detection, wind turbine BibRef

Xie, Y.X.[Yan-Xia], Sun, J.H.[Jun-Hua],
Robust lockwire segmentation with multiscale boundary-driven regional stability,
JOSA-A(40), No. 3, March 2023, pp. 397-410.
DOI Link 2503
Mechanical safety. Biomedical imaging, Deep learning, Image quality, Imaging techniques, Neural networks, Segmentation BibRef

Pham, D.[Dieuthuy], Ha, M.[Minhtuan], San, C.[Cao], Xiao, C.Y.[Chang-Yan],
Accurate stacked-sheet counting method based on deep learning,
JOSA-A(37), No. 7, July 2020, pp. 1206-1218.
DOI Link 2503
Biomedical imaging, Deep learning, Image quality, Imaging systems, Machine vision, Neural networks BibRef

Liu, Y.[Yi], Zhang, C.S.[Chang-Sheng], Dong, X.J.[Xing-Jun], Ning, J.X.[Jia-Xu],
Point Cloud-Based Deep Learning in Industrial Production: A Survey,
Surveys(57), No. 7, February 2025, pp. xx-yy.
DOI Link 2503
Deep learning, point cloud, industrial production, real-time BibRef

Costanzino, A.[Alex], Ramirez, P.Z.[Pierluigi Zama], Lisanti, G.[Giuseppe], di Stefano, L.[Luigi],
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping,
CVPR24(17234-17243)
IEEE DOI 2410
Point cloud compression, Memory management, Feature extraction, Manufacturing, Anomaly detection, anomaly, layer pruning BibRef

Rolih, B.[Blaž], Ameln, D.[Dick], Vaidya, A.[Ashwin], Akcay, S.[Samet],
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble,
VAND24(3866-3875)
IEEE DOI 2410
Training, Image resolution, Tiles, Memory management, Stacking, Graphics processing units, Anomaly Detection, High-resolution processing BibRef

Costanzino, A.[Alex], Ramirez, P.Z.[Pierluigi Zama], del Moro, M.[Mirko], Aiezzo, A.[Agostino], Lisanti, G.[Giuseppe], Salti, S.[Samuele], di Stefano, L.[Luigi],
Test Time Training for Industrial Anomaly Segmentation,
VAND24(3910-3920)
IEEE DOI 2410
Training, Quality control, Feature extraction, Standards, anomaly, anomaly detection, anomaly segmentation, anomaly scores BibRef

Lee, H.W.[Ho-Weng], Lai, S.H.[Shang-Hong],
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks,
VAND24(3921-3929)
IEEE DOI Code:
WWW Link. 2410
Location awareness, Training, Visualization, Source coding, Training data, Inspection, Feature extraction, Anomaly Detection, KSDD2 BibRef

Fang, Z.[Zheng], Wang, X.Y.[Xiao-Yang], Li, H.C.[Hao-Cheng], Liu, J.J.[Jie-Jie], Hu, Q.[Qiugui], Xiao, J.[Jimin],
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction,
ICCV23(17435-17444)
IEEE DOI Code:
WWW Link. 2401
BibRef

Sukel, M.[Maarten], Rudinac, S.[Stevan], Worring, M.[Marcel],
GIGO, Garbage In, Garbage Out: An Urban Garbage Classification Dataset,
MMMod23(I: 527-538).
Springer DOI 2304
BibRef

Artola, A.[Aitor], Kolodziej, Y.[Yannis], Morel, J.M.[Jean-Michel], Ehret, T.[Thibaud],
GLAD: A Global-to-Local Anomaly Detector,
WACV23(5490-5499)
IEEE DOI 2302
Anomalies in production. Adaptation models, Perturbation methods, Neural networks, Production, Machine learning, Detectors BibRef

Rudolph, M.[Marco], Wehrbein, T.[Tom], Rosenhahn, B.[Bodo], Wandt, B.[Bastian],
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection,
WACV23(2591-2601)
IEEE DOI 2302
Training, Location awareness, Neural networks, Estimation, Algorithms: Image recognition and understanding, object detection BibRef

Liu, C.[Chuang], Liu, J.[Jun], Zhang, M.J.[Mei-Juan], Liu, R.R.[Rui-Rui], Dong, G.F.[Guang-Feng], Wang, Z.[Zhen], Liu, Z.J.[Zhong-Jian],
Calculation of Salt Heap Volume Based on Point Cloud Surface Reconstruction,
ICRVC22(200-203)
IEEE DOI 2301
Point cloud compression, Training, Surface reconstruction, Solid modeling, Laser radar, Volume measurement, Point cloud, Volume BibRef

Tao, L.M.[Li-Ming], Xia, R.[Renbo], Zhao, J.B.[Ji-Bin], Li, Y.H.[Ying-Hao], Zou, H.[Hangbo], Wang, F.Y.[Fang-Yuan],
A High-Accuracy Slotted Hole Detector,
ICRVC22(136-141)
IEEE DOI 2301
Geometry, Image segmentation, Image edge detection, Fitting, Detectors, Filtering algorithms, projective invariant BibRef

Zhang, S.L.[Shi-Ling], Dai, L.J.[Liang-Jun], Deng, B.J.[Bao-Jia], Liu, Z.Q.[Zi-Qi],
Altitude Correction of Surface Control Field Strength of Converter Valve Hall Fittings Based on Ultraviolet Spectrum Image Analysis,
ICIVC22(818-824)
IEEE DOI 2301
Power equipment monitoring. Electrodes, Substations, Image databases, Fitting, High-voltage techniques, Insulators, Valves, infrared imager, simulation calculation BibRef

Zhang, S.L.[Shi-Ling],
Detection of Decomposition Products of SF6 Gas Based on Gas Chromatography and Optical Cavity Detection and Its Field Application,
ICIVC22(756-761)
IEEE DOI 2301
Heating systems, Semiconductor lasers, Circuit breakers, Sulfur hexafluoride, Metals, Hafnium, Detectors, latent defect BibRef

Xie, W.Z.[Wen-Zhuo], Wang, X.[Xuehua], Li, S.P.[Shi-Ping], Xu, W.[Wei], Duan, X.[Xianbao],
A Household Garbage Classification and Collection Device Based on Machine Vision and Deep Learning,
ICRVC22(209-214)
IEEE DOI 2301
Waste management, Training, Waste materials, Machine vision, Transfer learning, Software, Recycling, machine vision, MobileNetV2 BibRef

Kalitsios, G.[Georgios], Lazaridis, L.[Lazaros], Psaltis, A.[Athanasios], Axenopoulos, A.[Apostolos], Daras, P.[Petros],
Vision-Enhanced System For Human-Robot Disassembly Factory Cells: Introducing A New Screw Dataset,
ICRVC22(204-208)
IEEE DOI 2301
Visualization, Service robots, Semantic segmentation, Object detection, WEEE recycling, Robotic disassembly, Scene analysis BibRef

Agarwal, S.[Shivaank], Gudi, R.[Ravindra], Saxena, P.[Paresh],
One-Shot learning based classification for segregation of plastic waste,
DICTA20(1-3)
IEEE DOI 2201
Databases, Digital images, Plastics, Resins, Convolutional neural networks, Deep Learning, Plastic Waste Segregation BibRef

Yang, Z.J.[Zi-Jiang], Watari, T.[Tetsushi], Ichigozaki, D.[Daisuke], Mitsutoshi, A.[Akita], Takahashi, H.[Hiroaki], Suga, Y.[Yoshinori], Liao, W.K.[Wei-Keng], Choudhary, A.[Alok], Agrawal, A.[Ankit],
Heterogeneous Feature Fusion Based Machine Learning on Shallow-wide and Heterogeneous-sparse Industrial Datasets,
IML20(566-577).
Springer DOI 2103
BibRef

Li, Y.X.[Yi-Xin], Hu, F.[Fu], Qin, J.[Jian], Ryan, M.[Michael], Wang, R.[Ray], Liu, Y.[Ying],
A Hybrid Machine Learning Approach for Energy Consumption Prediction in Additive Manufacturing,
IML20(622-636).
Springer DOI 2103
BibRef

Rosati, R.[Riccardo], Romeo, L.[Luca], Cecchini, G.[Gianalberto], Tonetto, F.[Flavio], Perugini, L.[Luca], Ruggeri, L.[Luca], Viti, P.[Paolo], Frontoni, E.[Emanuele],
Bias from the Wild Industry 4.0: Are We Really Classifying the Quality or Shotgun Series?,
IML20(637-649).
Springer DOI 2103
BibRef

Berns, F.[Fabian], Ramsdorf, T.[Timo], Beecks, C.[Christian],
Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses,
IML20(650-661).
Springer DOI 2103
BibRef

Gan, B., Zhang, C.,
Research on the algorithm of urban waste classification and recycling based on deep learning technology,
CVIDL20(232-236)
IEEE DOI 2102
backpropagation, environmental science computing, image recognition, incineration, municipal solid waste, Migration learning BibRef

Nilsson, F., Jakobsen, J., Alonso-Fernandez, F.,
Detection and Classification of Industrial Signal Lights for Factory Floors,
ISCV20(1-6)
IEEE DOI 2011
factory automation, maintenance engineering, mass production, product customisation, production engineering computing, Computer Vision BibRef

Kassubeck, M., Malek, T., Mühlhausen, M., Kappel, M., Castillo, S., Dittrich, M., Magnor, M.,
Optical Quality Control for Adaptive Polishing Processes,
SSIAI20(90-94)
IEEE DOI 2009
cameras, computerised instrumentation, image processing, polishing, quality control, rendering (computer graphics), Polishing BibRef

Bormann, R.[Richard], de Brito, B.F.[Bruno Ferreira], Lindermayr, J.[Jochen], Omainska, M.[Marco], Patel, M.[Mayank],
Towards Automated Order Picking Robots for Warehouses and Retail,
CVS19(185-198).
Springer DOI 1912
BibRef

Yuan, B., Giera, B., Guss, G., Matthews, I., Mcmains, S.,
Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting,
WACV19(744-753)
IEEE DOI 1904
image classification, laser materials processing, learning (artificial intelligence), melting, neural nets, Convolutional neural networks BibRef

Brandão, S.[Susana], Marques, M.[Manuel],
Hot Tiles: A Heat Diffusion Based Descriptor for Automatic Tile Panel Assembly,
CVAA16(I: 768-782).
Springer DOI 1611
BibRef

Mueller, M., Voegtle, T.,
Determination of Steering Wheel Angles During Car Alignment By Image Analysis Methods,
ISPRS16(B5: 77-83).
DOI Link 1610
In industrial automation. BibRef

Duval, L., Moreaud, M., Couprie, C., Jeulin, D., Talbot, H., Angulo, J.,
Image processing for materials characterization: Issues, challenges and opportunities,
ICIP14(4862-4866)
IEEE DOI 1502
Image segmentation BibRef

Ulu, E.[Erva], Zhang, R.[Rusheng], Yumer, M.E.[Mehmet Ersin], Kara, L.B.[Levent Burak],
A Data-Driven Investigation and Estimation of Optimal Topologies under Variable Loading Configurations,
CompIMAGE14(387-399).
Springer DOI 1407
structural mechanics. BibRef

Garibotto, G.[Giovanni], Murrieri, P.[Pierpaolo],
White Paper on Industrial Applications of Computer Vision and Pattern Recognition,
CIAP13(II:721-730).
Springer DOI 1309
BibRef

Nagel, T.[Tim], Zhang, C.[Chao], Liu, S.[Steven],
Kalman Filter based leak localization applied to pneumatic systems,
ICARCV12(1777-1782).
IEEE DOI 1304
BibRef

Yu, H.L.[Hong-Liang], Liu, W.L.[Wan-Li], Dong, H.J.[Hui-Jun],
Research on recognition of working condition for calciner and grate cooler based on expert system,
ICARCV12(1733-1737).
IEEE DOI 1304
BibRef

Wang, X.H.[Xiao-Hong], Li, H.[Hui], Meng, Q.J.[Qing-Jin],
Design of process control system of rotary klin process for nickel iron production,
ICARCV12(1738-1742).
IEEE DOI 1304
BibRef

Wang, X.H.[Xiao-Hong], Wang, X.H.[Xiao-Hong], Lu, S.Z.[Shi-Zeng], Jing, S.H.[Shao-Hong],
Intelligence control method and application for decomposing furnace,
ICARCV12(1743-1748).
IEEE DOI 1304
BibRef

Yong, Y.[Yang],
Position variable structure control for water hydraulic vane actuator,
ICARCV12(1170-1174).
IEEE DOI 1304
BibRef

Crenganis, M.[Minai], Breaz, R.[Radu], Racz, G.[Gabriel], Bologa, O.[Octavian],
Inverse kinematics of a 7 DOF manipulator using Adaptive Neuro-Fuzzy Inference Systems,
ICARCV12(1232-1237).
IEEE DOI 1304
BibRef

Vardy, A.[Andrew],
Accelerated Patch Sorting by a Robotic Swarm,
CRV12(314-321).
IEEE DOI 1207
Vision to find clusters of objects and evaluate whether they match. BibRef

McNabb, K.A.[Kari-Ann],
Case studies of applying LiDAR for the electrical utility, mining, and water resources industries,
CGC10(212).
PDF File. 1006
BibRef

Sardis, E.S.,
Applying Multi-Agents Technologies in Industrial Plants,
WSSIP09(1-4).
IEEE DOI 0906
BibRef

Yilmazturk, F., Kulur, S., Terzi, N.,
Determination of Displacements in Load Tests with Digital Multimedia Photogrammetry,
ISPRS08(B5: 719 ff).
PDF File. 0807
BibRef

Li, J.S.[Jian-Song],
Optimizing Design and Analysis of Industrial Photogrammetric Network,
ISPRS08(B5: 95 ff).
PDF File. 0807
BibRef

Hirano, Y.[Yutaka], Garcia, C.[Christophe], Sukthankar, R.[Rahul], Hoogs, A.[Anthony],
Industry and Object Recognition: Applications, Applied Research and Challenges,
CLOR06(49-64).
Springer DOI 0711
BibRef

Sun, Y.[Yan], Fu, P.[Ping], Jiang, H.J.[Hua-Jun], Xiao, J.[Jun],
Automatic feed system based on machine vision,
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Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Factory Automation - General Vision Systems .