20.7.3.4 Inspection -- Metal Inspection, Castings, Machining

Decker, H.,
A Difference Technique for Automatic Inspection of Casting Parts,
PRL(2), 1983, pp. 125-129. BibRef 8300

Strecker, H.,
A Local Feature Method for the Detection of Flaws in Automated X-Ray Inspection of Castings,
SP(5), 1983, pp. 423-431. BibRef 8300

Batchelor, B.G., Cotter, S.M.,
Detection of Cracks Using Image Processing Algorithms Implemented in Hardware,
IVC(1), No. 1, February 1983, pp. 21-29.
Elsevier DOI BibRef 8302

Don, H.S., Fu, K.S., Liu, C.R., and Lin, W.C.,
Metal Surface Inspection Using Image Processing Techniques,
SMC(14), No. 1, January/February 1984, pp. 139-145. BibRef 8401

Suresh, B.R., Fundakowski, R.A., Levitt, T.S., and Overland, J.E.,
A Real-Time Automated Visual Inspection System for Hot Steel Slabs,
PAMI(5), No. 6, November 1983, pp. 563-572. Adaptive Threshold. Real time processing with off the shelf components - composed of a small array processor for segmentation and feature analysis and a mini for defect analysis. Roberts edge, adaptive threshold, find edges, link between scan lines, classify imperfections. BibRef 8311

Boerner, H., and Strecker, H.,
Automated X-Ray Inspection of Aluminum Castings,
PAMI(10), No. 1, January 1988, pp. 79-91.
IEEE DOI BibRef 8801

Okawa, Y.[Yoshikuni],
Automatic Inspection of the Surface Defects of Cast Metals,
CVGIP(25), No. 1, January 1984, pp. 89-112.
Elsevier DOI BibRef 8401

Piirainen, T., Silven, O., Pietikainen, M., Laitinen, T., Strommer, E.,
Automated Visual Inspection Of Rolled Metal Surfaces,
MVA(3), No. 4, 1990, pp. xx-yy. BibRef 9000

Marshall, A.D., Martin, R.R., Hutber, D.,
Automatic inspection of mechanical parts using geometric models and laser range finder data,
IVC(9), No. 6, December 1991, pp. 385-405.
Elsevier DOI 0401
BibRef

Meriaudeau, F., Renier, E., Truchetet, F.,
Temperature Imaging and Image-Processing in the Steel-Industry,
OptEng(35), No. 12, December 1996, pp. 3470-3481. 9701
BibRef

Lee, C., Peters, W.H., Chao, Y.J., Sutton, M.A.,
Improved Digital Image Processing Techniques to Investigate Plastic Zone Formation in Steel,
IVC(4), No. 4, November 1986, pp. 203-207.
Elsevier DOI BibRef 8611

Fellows, T.C.[Todd C.], Rittenhouse, N.E.[Norman E.], Yablonsky, P.J.[Peter J.],
Optical inspection system utilizing dynamic analog-to-digital thresholding,
US_Patent5,377,282, Dec 27, 1994
WWW Link. BibRef 9412

Bahr, B., Motavalli, S., Arfi, T.,
Sensor Fusion for Monitoring Machine-Tool Conditions,
JCIM(10), No. 5, September/October 1997, pp. 314-323. 9709
BibRef

Lou, K.N., Lin, C.J.,
An Intelligent Sensor Fusion System for Tool Monitoring on a Machining Center,
IJAMT(13), No. 8, 1997, pp. 556-565. 9712
BibRef

Garcia, D.F., Garcia, M., Obeso, F., Fernandez, V.,
Real-Time Flatness Inspection System for Steel Strip Production Lines,
RealTimeImg(5), No. 1, February 1999, pp. 35-47. BibRef 9902

Kassim, A.A.[Ashraf A.], Mannan, M.A., Jing, M.[Ma],
Machine tool condition monitoring using workpiece surface texture analysis,
MVA(11), No. 5, 2000, pp. 257-263.
Springer DOI 0004
BibRef

Mannan, M.A., Mian, Z.[Zhu], Kassim, A.A.,
Tool wear monitoring using a fast Hough transform of images of machined surfaces,
MVA(15), No. 3, July 2004, pp. 156-163.
Springer DOI 0407
BibRef

Kassim, A.A., Mian, Z.[Zhu], Mannan, M.A.,
Connectivity oriented fast Hough transform for tool wear monitoring,
PR(37), No. 9, September 2004, pp. 1925-1933.
Elsevier DOI 0407
BibRef

Kassim, A.A., Mian, Z.[Zhu], Mannan, M.A.,
Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures,
MVA(17), No. 5, October 2006, pp. 327-336.
Springer DOI 0609
BibRef
Earlier:
Texture analysis using fractals for tool wear monitoring,
ICIP02(III: 105-108).
IEEE DOI 0210
BibRef

Mannan, M.A., Kassim, A.A.[Ashraf A.], Jing, M.[Ma],
Application of image and sound analysis techniques to monitor the condition of cutting tools,
PRL(21), No. 11, October 2000, pp. 969-979. 0010
BibRef

Kassim, A.A., Mannan, M.A., Mian, Z.[Zhu],
Texture analysis methods for tool condition monitoring,
IVC(25), No. 7, 1 July 2007, pp. 1080-1090.
Elsevier DOI 0705
Machine vision; Texture analysis; Tool wear monitoring BibRef

Wiltschi, K.[Klaus], Pinz, A.[Axel], Lindeberg, T.[Tony],
An automatic assessment scheme for steel quality inspection,
MVA(12), No. 3, 2000, pp. 113-128.
Springer DOI 0010
BibRef

Li, X., Tso, S.K., Wang, J.,
Real-Time Tool Condition Monitoring Using Wavelet Transforms and Fuzzy Techniques,
SMC-C(30), No. 3, August 2000, pp. 352-357.
IEEE Top Reference. 0011
BibRef

Katafuchi, N.[Norifumi], Sano, M.[Mutsuo], Ohara, S.[Shuichi], Okudaira, M.[Masashi],
A method for inspecting industrial parts surfaces based on an optics model,
MVA(12), No. 4, 2000, pp. 170-176.
Springer DOI 0101
BibRef

Lee, M.F.R.[Min-Fan Ricky], de Silva, C.W.[Clarence W.], Croft, E.A.[Elizabeth A.], Wu, Q.M.J.[Q.M. Jonathan],
Machine vision system for curved surface inspection,
MVA(12), No. 4, 2000, pp. 177-188.
Springer DOI 0101
BibRef

Fraser, C.S.[Clive S.], Riedel, B.[Björn],
Monitoring the thermal deformation of steel beams via vision metrology,
PandRS(55), No. 4, November 2000, pp. 268-276. Monitored within 1mm. 0101
BibRef

Foresti, G.L.,
Visual inspection of sea bottom structures by an autonomous underwater vehicle,
SMC-B(31), No. 5, October 2001, pp. 691-705.
IEEE Top Reference. 0111
BibRef

Muller, U.[Ulrich], Peuker, G.[Gustav], Sonnenschein, D.[Detlef], Winter, D.[Detlef], Degner, M.[Michael], Thiemann, G.[Gerd],
Flatness measurement system for metal strip,
US_Patent6,286,349, Sep 11, 2001
WWW Link. BibRef 0109

Seulin, R.[Ralph], Merienne, F.[Frederic], Gorria, P.[Patrick],
Simulation of Specular Surface Imaging Based on Computer Graphics: Application on a Vision Inspection System,
JASP(2002), No. 7, July 2002, pp. 649-658. 0208
BibRef

Pernkopf, F.[Franz], O'Leary, P.[Paul],
Visual Inspection of Machined Metallic High-Precision Surfaces,
JASP(2002), No. 7, July 2002, pp. 667-678. 0208
BibRef

Garcia, D.F.[Daniel F.], Usamentiaga, R.[Rubén], Marín, I.[Ignacio], González, J.A.[Juan A.], de Abajo, N.[Nicolas],
Shape Inspection System for Variable-Luminance Steel Plates with Real-Time Adaptation Capabilities to Luminance Variations,
RealTimeImg(8), No. 4, August 2002, pp. 303-315.
DOI Link 0210
BibRef

Fish, R.K., Ostendorf, M., Bernard, G.D., Castanon, D.A.,
Multilevel classification of milling tool wear with confidence estimation,
PAMI(25), No. 1, January 2003, pp. 75-85.
IEEE DOI 0301
BibRef

Tsai, D.M.[Du-Ming], Huang, T.Y.[Tse-Yun],
Automated surface inspection for statistical textures,
IVC(21), No. 4, April 2003, pp. 307-323.
Elsevier DOI 0301
BibRef

Mäenpää, T.[Topi], Turtinen, M.[Markus], Pietikäinen, M.[Matti],
Real-time surface inspection by texture,
RealTimeImg(9), No. 5, October 2003, pp. 289-296.
Elsevier DOI 0311
BibRef

Zhang, Y.J.[Yong-Jun], Zhang, Z.X.[Zu-Xun], Zhang, J.Q.[Jian-Qing],
Deformation visual inspection of industrial parts with image sequence,
MVA(15), No. 3, July 2004, pp. 115-120.
Springer DOI 0407
BibRef

Zhang, Y.J.[Yong-Jun], Zhang, Z.X.[Zu-Xun], Zhang, J.Q.[Jian-Qing],
Automatic measurement of industrial sheetmetal parts with CAD data and non-metric image sequence,
CVIU(102), No. 1, April 2006, pp. 52-59.
Elsevier DOI Photogrammetry; Industry; Part; CAD; Image sequence; Matching; Reconstruction; Measurement 0604
BibRef

Zheng, S.Y.[Shun-Yi], Zhai, R.F.[Rui-Fang], Zhang, Z.X.[Zu-Xun],
Generation of 3D Surface Model of Complex Objects Based on Non-Metric Camera,
ICIP07(III: 89-92).
IEEE DOI 0709
BibRef

Bellini, P., Bruno, I., Nesi, P.,
A distributed system for computer vision quality control of clinched boards,
RealTimeImg(10), No. 3, June 2004, pp. 161-176.
Elsevier DOI 0410
Clinching technology allows to join metal sheets by using a cold press. BibRef

Pernkopf, F.[Franz],
3D surface analysis using coupled HMMs,
MVA(16), No. 5, December 2005, pp. 298-305.
Springer DOI 0601
BibRef
Earlier:
3D surface inspection using coupled HMMs,
ICPR04(III: 223-226).
IEEE DOI 0409
BibRef

Li, D.G.[Dong-Guang],
Ballistics Projectile Image Analysis for Firearm Identification,
IP(15), No. 10, October 2006, pp. 2857-2865.
IEEE DOI 0609
BibRef

Li, D.G.[Dong-Guang],
Visual Information for Firearm Identification by Digital Holography,
Visual07(445-452).
Springer DOI 0706
BibRef

Khalili, K.[Khalil], Webb, P.[Philip],
The development and application of a multiple wavelength illumination technique for the vision-based process monitoring of aero-structure riveting,
MVA(18), No. 2, April 2007, pp. 73-83.
Springer DOI 0704
BibRef

Cao, J.H.[Jian-Hai], Lu, C.H.[Chang-Hou], Shi, C.Y.[Chun-Yi],
Product quality on-line inspecting for the pressed protuberant character on a metal tag,
IVC(25), No. 8, 1 August 2007, pp. 1255-1262.
Elsevier DOI 0706
Pressed protuberant character; Ring projection; Vector sum; Quality inspecting BibRef

Lin, H.D.[Hong-Dar],
Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach,
IVC(25), No. 11, 1 November 2007, pp. 1785-1801.
Elsevier DOI 0709
Ceramic Capacitors. Detection of ripple defects; Machine vision; Hotelling T2 multivariate statistics; Wavelet characteristics BibRef

Liu, Z., Genest, M., Marincak, A., Forsyth, D.S.,
Characterization of surface deformation with the Edge of LightTM technique,
MVA(19), No. 1, January 2008, pp. 35-42.
Springer DOI 0801
Aircraft joint inspection where deformation is due to hidden corrosion. BibRef

Forsyth, D.S., Marincak, A., Komorowski, J.P.,
Edge of Light: A new enhanced optical NDI technique,
SPIE(2495), 1995, pp. 178-188. BibRef 9500

Caulier, Y.[Yannick], Spinnler, K.[Klaus], Bourennane, S.[Salah], Wittenberg, T.[Thomas],
New Structured Illumination Technique for the Inspection of High-Reflective Surfaces: Application for the Detection of Structural Defects without any Calibration Procedures,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0804

See also Visually Inspecting Specular Surfaces: A Generalized Image Capture and Image Description Approach. BibRef

Caulier, Y.[Yannick], Bourennane, S.[Salah],
Fourier-Based Inspection of Free-Form Reflective Surfaces,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Huang, Q., Wu, Y., Baruch, J., Jiang, P., Peng, Y.,
A Template Model for Defect Simulation for Evaluating Nondestructive Testing in X-Radiography,
SMC-A(39), No. 2, March 2009, pp. 466-475.
IEEE DOI 0903
BibRef

Fabijanska, A.[Anna], Sankowski, D.[Dominik],
Computer vision system for high temperature measurements of surface properties,
MVA(20), No. 6, October 2009, pp. xx-yy.
Springer DOI 0910
Deal with issues of high temperature. BibRef

Besnard, G.[Gilles], Lagrange, J.M.[Jean-Michel], Hild, F.[François], Roux, S.[Stéphane], Voltz, C.[Christophe],
Characterization of Necking Phenomena in High-Speed Experiments by Using a Single Camera,
JIVP(2010), No. 2010, pp. xx-yy.
DOI Link 1003
Metal strain analysis. BibRef

Duan, G.F.[Gui-Fang], Chen, Y.W.[Yen-Wei], Sukegawa, T.[Takeshi],
Automatic optical flank wear measurement of microdrills using level set for cutting plane segmentation,
MVA(21), No. 5, August 2010, pp. 667-676.
WWW Link. 1011
BibRef

Lughofer, E.[Edwin], Smith, J.E.[James E.], Tahir, M.A.[Muhammad Atif], Caleb-Solly, P.[Praminda], Eitzinger, C.[Christian], Sannen, D.[Davy], Nuttin, M.[Marnix],
Human-Machine Interaction Issues in Quality Control Based on Online Image Classification,
SMC-A(39), No. 5, September 2009, pp. 960-971.
IEEE DOI 0909
BibRef
Earlier: A6, A7, A2, A3, A4, A1, A5:
An On-Line Interactive Self-adaptive Image Classification Framework,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Lughofer, E.[Edwin],
On-line evolving image classifiers and their application to surface inspection,
IVC(28), No. 7, July 2010, pp. 1065-1079.
Elsevier DOI 1006
Evolving image classifiers; Incremental learning; Evolving vector quantization; Evolving fuzzy classifiers; On-line surface inspection systems BibRef

Eitzinger, C.[Christian], Gmainer, M.[Manfred], Heidl, W.[Wolfgang], Lughofer, E.[Edwin],
Increasing Classification Robustness with Adaptive Features,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Eitzinger, C.[Christian], Heidl, W., Lughofer, E., Raiser, S., Smith, J.E., Tahir, M.A., Sannen, D., van Brussel, H.,
Assessment of the influence of adaptive components in trainable surface inspection systems,
MVA(21), No. 5, August 2010, pp. 613-626.
WWW Link. 1011
BibRef

Raiser, S.[Stefan], Lughofer, E.[Edwin], Eitzinger, C.[Christian], Smith, J.E.[James Edward],
Impact of object extraction methods on classification performance in surface inspection systems,
MVA(21), No. 5, August 2010, pp. 627-641.
WWW Link. 1011
BibRef

Tabata, T.[Tomohira], Komuro, T.[Takashi], Ishikawa, M.[Masatoshi],
Surface image synthesis of moving spinning cans using a 1,000-fps area scan camera,
MVA(21), No. 5, August 2010, pp. 643-652.
WWW Link. 1011
Inspection based on mosaic creation for spinning cylinder. BibRef

Martin, D., Guinea, D.M., García-Alegre, M.C., Villanueva, E., Guinea, D.,
Multi-modal defect detection of residual oxide scale on a cold stainless steel strip,
MVA(21), No. 5, August 2010, pp. 653-666.
WWW Link. 1011
BibRef

Nakazawa, M.[Mitsuru], Kobayashi, M.[Masakazu], Toda, H.[Hiroyuki], Aoki, Y.[Yoshimitsu],
Proposal of a method to analyze 3D deformation/fracture characteristics inside materials based on a stratified matching approach,
MVA(21), No. 5, August 2010, pp. 687-694.
WWW Link. 1011
BibRef

Perng, D.B.[Der-Baau], Chen, Y.C.[Yen-Chung],
An advanced auto-inspection system for micro-router collapse,
MVA(21), No. 6, October 2010, pp. 811-824.
WWW Link. 1011
tool to cut PCB. BibRef

Carrasco, M.[Miguel], Mery, D.[Domingo],
Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels,
MVA(22), No. 1, January 2011, pp. 157-170.
WWW Link. 1101
BibRef

Mery, D.[Domingo], Riffo, V.[Vladimir], Zuccar, I.[Irene], Pieringer, C.[Christian],
Automated X-Ray Object Recognition Using an Efficient Search Algorithm in Multiple Views,
PBVS13(368-374)
IEEE DOI 1309
3D object recognition BibRef

Mery, D.[Domingo],
Automated detection in complex objects using a tracking algorithm in multiple X-ray views,
OTCBVS11(41-48).
IEEE DOI 1106
BibRef

Osman, A.[Ahmad], Kaftandjian, V.[Valerie], Hassler, U.[Ulf],
Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory,
PRL(32), No. 2, 15 January 2011, pp. 168-180.
Elsevier DOI 1101
X-ray imaging; Castings inspection; Features extraction; Data fusion; Confidence levels; Dempster-Shafer theory BibRef

Davis, T.A.[Tyler A.], Shin, Y.C.[Yung C.],
Vision-based clad height measurement,
MVA(22), No. 1, January 2011, pp. 129-136.
WWW Link. 1101
Laser deposition process. How much is deposited. BibRef

Tellaeche, A., Arana, R.,
Three-dimensional machine vision and machine-learning algorithms applied to quality control of percussion caps,
IET-CV(5), No. 2, 2011, pp. 117-124.
DOI Link 1103
BibRef

Sridhar, V.G., Adithan, M.,
Evaluation of the surface finish of ground components and study on lighting conditions in machine vision system,
IJCVR(2), No. 2, 2011, pp. 141-155.
DOI Link 1109
BibRef

Brancaccio, A.[Adriana], Leone, G.[Giovanni], Solimene, R.[Raffaele],
Fault detection in metallic grid scattering,
JOSA-A(28), No. 12, December 2011, pp. 2588-2599.
WWW Link. 1112
BibRef

Bokhabrine, Y.[Youssef], Seulin, R.[Ralph], Voon, L.F.C.L.Y.[Lew F. C. Lew Yan], Gorria, P.[Patrick], Girardin, G.[Gouenou], Gomez, M.[Miguel], Jobard, D.[Daniel],
3D characterization of hot metallic shells during industrial forging,
MVA(23), No. 3, May 2012, pp. 417-425.
WWW Link. 1204
Using 2 ToF laser systems. BibRef

Leloglu, U.M.,
Characterisation of tool marks on cartridge cases by combining multiple images,
IET-IPR(6), No. 7, 2012, pp. 854-862.
DOI Link 1211
BibRef

Palani, S., Natarajan, U., Chellamalai, M.,
On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS),
MVA(24), No. 1, January 2013, pp. 19-32.
WWW Link. 1301
machining tools. BibRef

Sills, K.[Ken], Bone, G.M.[Gary M.], Capson, D.[David],
Defect identification on specular machined surfaces,
MVA(25), No. 2, February 2014, pp. 377-388.
WWW Link. 1412
BibRef

Zhao, X.Y.[Xin-Yue], He, Z.X.[Zai-Xing], Zhang, S.Y.[Shu-You],
Defect detection of castings in radiography images using a robust statistical feature,
JOSA-A(31), No. 1, January 2014, pp. 196-205.
DOI Link 1412
Image analysis; Defect understanding BibRef

Ali, M.H.[M. Hazrat], Kurokawa, S.[Syuhei], Uesugi, K.[Kensuke],
Application of machine vision in improving safety and reliability for gear profile measurement,
MVA(25), No. 6, 2014, pp. 1549-1559.
WWW Link. 1408
BibRef

Shreve, M.[Matthew], Pamplona, M.[Mauricio], Luguev, T.[Timur], Goldgof, D.[Dmitry], Sarkar, S.[Sudeep],
High-resolution 3D surface strain magnitude using 2D camera and low-resolution depth sensor,
PRL(50), No. 1, 2014, pp. 34-42.
Elsevier DOI 1410
3-D BibRef

Neogi, N.[Nirbhar], Mohanta, D.[Dusmanta], Dutta, P.[Pranab],
Review of vision-based steel surface inspection systems,
JIVP(2014), No. 1, 2014, pp. 50.
DOI Link 1411
BibRef

Xu, K.[Ke], Liu, S.H.[Shun-Hua], Ai, Y.H.[Yong-Hao],
Application of Shearlet transform to classification of surface defects for metals,
IVC(35), No. 1, 2015, pp. 23-30.
Elsevier DOI 1503
Surface inspection BibRef

Li, X.W.[Xiao-Wen], Li, P.[Ping], Lin, Z.[Zhuang], Yang, D.M.[Dong-Mei],
Analysis and Optimization of Composite to Steel Joints for Ships,
Sensors(182), No. 11, November 2014, pp. 10-16.
HTML Version. 1504
BibRef

Wang, L.[Lei], Zhang, W.[Wei], Zhang, J.C.[Jian-Cheng], Weng, J.J.[Jian-Jian], He, S.[Shan],
Visual Inspection for Breakage of Micro-milling Cutter,
Sensors(182), No. 11, November 2014, pp. 217-222.
HTML Version. 1504
BibRef

Fernández-Robles, L.[Laura], Azzopardi, G.[George], Alegre, E.[Enrique], Petkov, N.[Nicolai],
Cutting Edge Localisation in an Edge Profile Milling Head,
CAIP15(II:336-347).
Springer DOI 1511
BibRef

Liu, M.[Maofu], Liu, Y.[Ya], Hu, H.J.[Hui-Jun], Nie, L.Q.[Li-Qiang],
Genetic algorithm and mathematical morphology based binarization method for strip steel defect image with non-uniform illumination,
JVCIR(37), No. 1, 2016, pp. 70-77.
Elsevier DOI 1603
Strip steel defect image BibRef

Funes, J.F.[José Félix], Perales, J.M.[José Manuel], Rapún, M.L.[María-Luisa], Vega, J.M.[José Manuel],
Defect Detection from Multi-frequency Limited Data via Topological Sensitivity,
JMIV(55), No. 1, May 2016, pp. 19-35.
Springer DOI 1604
BibRef

Keglevic, M.[Manuel], Sablatnig, R.[Robert],
Retrieval of Striated Toolmarks Using Convolutional Neural Networks,
IET-CV(11), No. 7, October 2017, pp. 613-619.
DOI Link 1709
BibRef
Earlier:
FORMS-Locks: A Dataset for the Evaluation of Similarity Measures for Forensic Toolmark Images,
MedForen17(1890-1897)
IEEE DOI 1709

See also Writer Identification and Retrieval Using a Convolutional Neural Network. Cloning, Forensics, Image edge detection, Lighting, Manuals, Microscopy, Tools. Lock cylinders, damage from crimes. BibRef

Ren, R., Hung, T., Tan, K.C.,
A Generic Deep-Learning-Based Approach for Automated Surface Inspection,
Cyber(48), No. 3, March 2018, pp. 929-940.
IEEE DOI 1802
Feature extraction, Heating, Inspection, Object recognition, Surface morphology, Training, segmentation BibRef

Yang, J.H.[Jing-Hao], Liu, W.[Wei], Zhang, R.[Renwei], Jia, Z.Y.[Zhen-Yuan], Wang, F.[Fuji], Li, S.J.[Shi-Jie],
A method for measuring the thermal geometric parameters of large hot rectangular forgings based on projection feature lines,
MVA(29), No. 3, April 2018, pp. 467-476.
Springer DOI 1804
BibRef

Maglietta, R.[Rosalia], Milella, A.[Annalisa], Caccia, M.[Massimo], Bruzzone, G.[Gabriele],
A vision-based system for robotic inspection of marine vessels,
SIViP(12), No. 3, March 2018, pp. 471-478.
Springer DOI 1804
Magnetic climbing robot. Hull inspection. BibRef

Paul, A.[Angshuman], Gangopadhyay, A.[Abhinandan], Chintha, A.R.[Appa Rao], Mukherjee, D.P.[Dipti Prasad], Das, P.[Prasun], Kundu, S.[Saurabh],
Calculation of phase fraction in steel microstructure images using random forest classifier,
IET-IPR(12), No. 8, August 2018, pp. 1370-1377.
DOI Link 1808
BibRef

Yang, K.[Kai], Sun, Z.Y.[Zhi-Yi], Wang, A.H.[An-Hong], Liu, R.Z.[Rui-Zhen], Sun, Q.L.[Qian-Lai], Wang, Y.[Yin],
Deep hashing network for material defect image classification,
IET-CV(12), No. 8, December 2018, pp. 1112-1120.
DOI Link 1812
BibRef

Jachym, M.[Marc], Lavernhe, S.[Sylvain], Euzenat, C.[Charly], Tournier, C.[Christophe],
Effective NC machining simulation with OptiX ray tracing engine,
VC(35), No. 2, February 2019, pp. 281-288.
WWW Link. 1906
BibRef

Han, L.[Lili], Li, S.J.[Shu-Juan], Liu, X.P.[Xiu-Ping], Guo, J.[Jiaan],
Online burr video denoising by learning sparsifying transform,
IET-IPR(13), No. 7, 30 May 2019, pp. 1138-1145.
DOI Link 1906
Burrs on high-voltage copper contact leads to device damage. BibRef

Panda, A.[Aditi], Naskar, R.[Ruchira], Pal, S.[Snehanshu],
Deep learning approach for segmentation of plain carbon steel microstructure images,
IET-IPR(13), No. 9, 18 July 2019, pp. 1516-1524.
DOI Link 1907
BibRef

Wang, W., Qu, Z., Zheng, Z., Kelvin, S.Y.P.[S. Y. Phua], Ivan, C., See, K.Y., Zheng, Y.,
Analysis and Design of Coil-Based Electromagnetic-Induced Thermoacoustic for Rail Internal-Flaw Inspection,
ITS(20), No. 7, July 2019, pp. 2691-2702.
IEEE DOI 1907
Rails, Magnetic resonance imaging, Acoustics, Ferrites, Electromagnetics, Inspection, Heating systems, multiphysics BibRef

He, D.[Di], Xu, K.[Ke], Wang, D.D.[Da-Dong],
Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels,
IVC(89), 2019, pp. 12-20.
Elsevier DOI 1909
AutoEncoder, Convolutional neural networks, Defect identification, Hot rolled steels, Surface inspection BibRef

Pratama, M., Dimla, E., Tjahjowidodo, T., Pedrycz, W., Lughofer, E.,
Online Tool Condition Monitoring Based on Parsimonious Ensemble+,
Cyber(50), No. 2, February 2020, pp. 664-677.
IEEE DOI 1912
Tools, Complexity theory, Merging, Machining, Sensors, Condition monitoring, Monitoring, Concept drifts, prognostic health management BibRef

Huang, Y.B.[Yi-Bin], Qiu, C.Y.[Cong-Ying], Yuan, K.[Kui],
Surface defect saliency of magnetic tile,
VC(36), No. 1, January 2020, pp. 85-96.
WWW Link. 2001
BibRef

Zhang, G.Z.[Gui-Zhong], Wang, B.X.[Bao-Xian], Yan, Z.B.[Zhao-Bo], Li, Y.Q.A.[Yi-Qi-Ang], Yang, H.Z.[Huai-Zhi],
Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion,
IEICE(E103-D), No. 2, February 2020, pp. 450-453.
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Performance evaluation of various texture analysis techniques for machine vision-based characterisation of machined surfaces,
IJCVR(10), No. 3, 2020, pp. 242-259.
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Digital Assisted Image Correlation for Metal Sheet Strain Measurement,
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Lin, J.H.[Jin-Hua], Ma, L.[Lin],
Fast restoration of geometric details of automobile castings scanned by RGB-D sensor,
RealTimeIP(17), No. 4, August 2020, pp. 871-886.
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Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
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Zhang, Y.[Ye], Hao, Q.A.[Qi-Ang], Cai, G.Q.[Guo-Qiang], Lv, J.J.[Jiao-Jiao], Yang, C.[Chen],
Crack damage identification and localisation on metro train bogie frame in IoT using guided waves,
IET-ITS(14), No. 11, November 2020, pp. 1403-1409.
DOI Link 2010
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di Cataldo, S., Vinco, S., Urgese, G., Calignano, F., Ficarra, E., Macii, A., Macii, E.,
Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions,
PIEEE(109), No. 4, April 2021, pp. 326-346.
IEEE DOI 2104
Optimization, Powders, Laser fusion, Laser sintering, Three-dimensional printing, Laser beams, smart manufacturing BibRef

Takimoto, Y., Harakawa, R., Iwahashi, M.,
Hue-Based Gray Coding Method for Three-Dimensional Surface Measurement of Cutlery With Specular Reflection,
CirSysVideo(31), No. 4, April 2021, pp. 1323-1331.
IEEE DOI 2104
Encoding, Brightness, Image color analysis, Reflective binary codes, Calibration, Surface treatment, cutlery BibRef

Xiang, Z.[Zhong], Wu, H.X.[Hua-Xiong], Zhou, D.[Ding],
Metallic debossed characters industrial online non-segmentation identification based on improved multi-scale image fusion enhancement and deep neural network,
IET-IPR(16), No. 3, 2022, pp. 852-868.
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Hu, B.[Bin], Wang, X.G.[Xing-Gang], Yu, W.Y.[Wen-Yong],
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SP:IC(107), 2022, pp. 116807.
Elsevier DOI 2208
Weakly supervised learning, Defect detection, Semantic segmentation BibRef

Li, W.R.[Wan-Run], Zhao, W.H.[Wen-Hai], Gu, J.[Jiaze], Fan, B.Y.[Bo-Yuan], Du, Y.F.[Yong-Feng],
Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Sun, L.S.[Lian-Shan], Wei, J.X.[Jing-Xue], Du, H.C.[Han-Chao], Zhang, Y.B.[Yong-Bin], He, L.F.[Li-Feng],
MSFF: A Multi-Scale Feature Fusion Network for Surface Defect Detection of Aluminum Profiles,
IEICE(E105-D), No. 9, September 2022, pp. 1652-1655.
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Huang, J.C.[Jian-Cai], Jiang, Z.H.[Zhao-Hui], Gui, W.H.[Wei-Hua], Yi, Z.H.[Zun-Hui], Pan, D.[Dong], Zhou, K.[Ke], Xu, C.[Chuan],
Depth Estimation from a Single Image of Blast Furnace Burden Surface Based on Edge Defocus Tracking,
CirSysVideo(32), No. 9, September 2022, pp. 6044-6057.
IEEE DOI 2209
Surface topography, Surface treatment, Estimation, Surface charging, Surface morphology, Endoscopes, eight-direction depth gradient template BibRef

Hu, X.X.[Xiao-Xi], Cao, Y.[Yuan], Sun, Y.[Yongkui], Tang, T.[Tao],
Railway Automatic Switch Stationary Contacts Wear Detection Under Few-Shot Occasions,
ITS(23), No. 9, September 2022, pp. 14893-14907.
IEEE DOI 2209
Feature extraction, Size measurement, Rail transportation, Current measurement, Task analysis, Switches, Contacts, key point detection BibRef

Algredo-Badillo, I.[Ignacio], Portillo-García, G.[Germàn], Ramírez-Gutiérrez, K.A.[Kelsey A.], Morales-Rosales, L.A.[Luis A.],
Irregularities recognition system for automotive pieces,
IJCVR(12), No. 5, 2022, pp. 614-631.
DOI Link 2211
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Joshi, K.[Ketaki], Patil, B.[Bhushan],
Automated inspection of spur gears using machine vision approach,
IJCVR(13), No. 1, 2023, pp. 38-51.
DOI Link 2212
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Tang, B.[Bo], Chen, L.[Li], Sun, W.[Wei], Lin, Z.K.[Zhong-Kang],
Review of surface defect detection of steel products based on machine vision,
IET-IPR(17), No. 2, 2023, pp. 303-322.
DOI Link 2302
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Shashikala, T.D., Sunitha, B.L., Basavarajappa, S., Davim, J.P.,
Some Studies on Measurement of Worn Surface by Digital Image Processing,
IJIG(23), No. 2 2023, pp. 2350016.
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Tang, B.[Bo], Song, Z.K.[Zi-Kai], Sun, W.[Wei], Wang, X.D.[Xing-Dong],
An end-to-end steel surface defect detection approach via Swin transformer,
IET-IPR(17), No. 5, 2023, pp. 1334-1345.
DOI Link 2304
feature extraction, image classification, image recognition BibRef

Hou, X.Q.[Xiu-Quan], Liu, M.Q.[Mei-Qin], Zhang, S.[Senlin], Wei, P.[Ping], Chen, B.D.[Ba-Dong],
CANet: Contextual Information and Spatial Attention Based Network for Detecting Small Defects in Manufacturing Industry,
PR(140), 2023, pp. 109558.
Elsevier DOI 2305
Small defect detection, Contextual information, Spatial attention, Multi-scale feature fusion, Automatic visual inspection BibRef

Liu, Z.[Zepeng], Lang, Z.Q.[Zi-Qiang], Zhu, Y.P.[Yun-Peng], Gui, Y.F.[Yu-Fei], Laalej, H.[Hatim], Stammers, J.[Jon],
Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining,
SMCS(53), No. 5, May 2023, pp. 2641-2653.
IEEE DOI 2305
Feature extraction, Analytical models, Data models, Cutting tools, Milling, Hidden Markov models, Vibrations, Advance manufacturing, systems engineering BibRef

Cao, X.C.[Xin-Cheng], Yao, B.[Bin], Chen, B.Q.[Bin-Qiang], He, W.P.[Wang-Peng], Guo, S.Q.[Su-Qin], Chen, K.[Kun],
Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network,
IEICE(E106-D), No. 5, May 2023, pp. 644-652.
WWW Link. 2305
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Yang, B.[Benyi], Liu, Z.Y.[Zhen-Yu], Duan, G.F.[Gui-Fang], Tan, J.R.[Jiang-Rong],
Residual shape adaptive dense-nested Unet: Redesign the long lateral skip connections for metal surface tiny defect inspection,
PR(147), 2024, pp. 110073.
Elsevier DOI 2312
Machine vision, Metal surface defect inspection, Dense-nested Unet, Residual Shape Adaptive module BibRef

Bringier, B.[Benjamin], Khoudeir, M.[Majdi],
High-speed optical 3D measurement device for quality control of aircraft rivet,
IJCVR(14), No. 1, 2024, pp. 1-17.
DOI Link 2312
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Xu, H.[Haohao], Xu, S.C.[Shu-Chang], Yang, W.Z.[Wen-Zhen],
Unsupervised industrial anomaly detection with diffusion models,
JVCIR(97), 2023, pp. 103983.
Elsevier DOI 2312
Anomaly detection, Diffusion model, Image reconstruction, Unsupervised learning BibRef

Wang, P.Y.[Peng-Yu], Jing, P.[Peng],
Deep learning-based methods for detecting defects in cast iron parts and surfaces,
IET-IPR(18), No. 1, 2024, pp. 47-58.
DOI Link 2401
convolutional neural nets, deburring, image fusion, learning (artificial intelligence) BibRef

Liu, Z.F.[Zhou-Feng], Guo, Z.J.[Zi-Jing], Li, C.L.[Chun-Lei], Huang, N.[Ning], Gao, C.L.[Chang-Le],
FSSDD: Few-shot steel defect detection based on multi-scale semantic enhancement representation and mask category information mapping,
IET-IPR(18), No. 1, 2024, pp. 88-104.
DOI Link 2401
computer vision, convolutional neural nets, object detection, steel BibRef

Yang, H.L.[Hai-Long], Liu, Y.H.[Ying-Hao], Xia, T.[Tian],
Defect Detection Scheme of Pins for Aviation Connectors Based on Image Segmentation and Improved RESNET-50,
IJIG(24), No. 1, Januaur 2024, pp. 2450011.
DOI Link 2402
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Fan, J.C.[Jia-Cheng], Wang, M.[Min], Li, B.[Baolei], Liu, M.X.[Ming-Xue], shen, D.[Dingcai],
ACD-YOLO: Improved YOLOv5-based method for steel surface defects detection,
IET-IPR(18), No. 3, 2024, pp. 761-771.
DOI Link 2402
defect detection, feature fusion, genetic algorithm, steel surface defects, yolov5 BibRef

Guo, J.[Jun], Yang, Y.[Yang], Xiong, X.[Xin], Yang, Y.[Yue], Shao, M.Z.[Meng-Zhen],
Brake disc positioning and defect detection method based on improved Canny operator,
IET-IPR(18), No. 5, 2024, pp. 1283-1295.
DOI Link 2404
image denoising, image processing, image recognition, image segmentation BibRef

Zhang, Y.[Yongde], Wang, W.[Wei], Guo, Z.H.[Zhong-Hua], Ji, Y.C.[Yang-Chun],
Research on oriented surface defect detection in the aircraft skin-coating process based on an attention detector,
IET-IPR(18), No. 5, 2024, pp. 1213-1228.
DOI Link 2404
convolutional neural nets, image processing, image recognition, pattern recognition, quality control, vision defects BibRef

Liu, L.[Li], Liu, Z.J.[Zi-Jin], Hou, A.[Aishan], Qian, X.F.[Xue-Fei], Wang, H.[Hui],
Adaptive edge detection of rebar thread head image based on improved Canny operator,
IET-IPR(18), No. 5, 2024, pp. 1145-1160.
DOI Link 2404
adaptive edge detection, filtering algorithm, image processing, improved Canny operator BibRef

Shao, L.[Lei], He, J.W.[Jia-Wei], Lu, X.[Xia], Hei, B.[Bo], Qu, J.H.[Jia-Hao], Liu, W.H.[Wei-Hua],
Aircraft Skin Damage Detection and Assessment From UAV Images Using GLCM and Cloud Model,
ITS(25), No. 3, March 2024, pp. 3191-3200.
IEEE DOI 2405
Aircraft, Skin, Atmospheric modeling, Maintenance engineering, Autonomous aerial vehicles, Feature extraction, GLCM BibRef

Xu, C.[Chuan], Jiang, Z.H.[Zhao-Hui], Pan, D.[Dong], Yu, H.Y.[Hao-Yang], Huang, J.C.[Jian-Cai], Zhou, K.[Ke], Gui, W.H.[Wei-Hua],
Multiscale Neighborhood Adaptive Clustering Image Segmentation for Molten Iron Flow Slag-Iron Recognition,
SMCS(54), No. 8, August 2024, pp. 4642-4654.
IEEE DOI 2408
Iron, Slag, Image segmentation, Image recognition, Monitoring, Temperature measurement, Clustering algorithms, slag-iron recognition BibRef

Bae, J.[Jaehyeok], Lee, J.H.[Jae-Han], Kim, S.[Seyun],
PNI: Industrial Anomaly Detection using Position and Neighborhood Information,
ICCV23(6350-6360)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhang, X.Y.[Xin-Yi], Li, N.Q.[Nai-Qi], Li, J.W.[Jia-Wei], Dai, T.[Tao], Jiang, Y.[Yong], Xia, S.T.[Shu-Tao],
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model,
ICCV23(6759-6768)
IEEE DOI 2401
BibRef

Lu, F.[Fanbin], Yao, X.F.[Xu-Feng], Fu, C.W.[Chi-Wing], Jia, J.Y.[Jia-Ya],
Removing Anomalies as Noises for Industrial Defect Localization,
ICCV23(16120-16129)
IEEE DOI 2401
BibRef

Intxausti, E.[Eneko], Zugasti, E.[Ekhi], Cernuda, C.[Carlos], Leibar, A.M.[Ane Miren], Elizondo, E.[Estibaliz],
Towards Robust Defect Detection in Casting Using Contrastive Learning,
CIARP23(I:605-616).
Springer DOI 2312
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Mallem, S.[Soufiane], Nakib, A.[Amir],
Towards Robustness: Enhancing Deep Learning Models Through Meta-Learning and Bilevel Optimization for Accurate Car Damage Classification,
ICIP23(107-115)
IEEE DOI 2312
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Pérez-Gonzalo, R.[Raül], Espersen, A.[Andreas], Agudo, A.[Antonio],
Robust Wind Turbine Blade Segmentation from RGB Images in the Wild,
ICIP23(1025-1029)
IEEE DOI 2312
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Fulir, J.[Juraj], Bosnar, L.[Lovro], Hagen, H.[Hans], Gospodnetic, P.[Petra],
Synthetic Data for Defect Segmentation on Complex Metal Surfaces,
VISION23(4424-4434)
IEEE DOI 2309
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Lv, C.[Chengkan], Zhang, Z.T.[Zheng-Tao], Shen, F.[Fei], Zhang, F.[Feng],
Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification,
VISION23(4435-4444)
IEEE DOI 2309
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Wei, J.[Jing], Shen, F.[Fei], Lv, C.[Chengkan], Zhang, Z.T.[Zheng-Tao], Zhang, F.[Feng], Yang, H.[Huabin],
Diversified and Multi-Class Controllable Industrial Defect Synthesis for Data Augmentation and Transfer,
VISION23(4445-4453)
IEEE DOI 2309
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Liao, J.S.[Jun-Song], Yang, L.[Lemiao], Tan, H.S.[Hai-Shu], Zhou, F.Q.[Fu-Qiang],
A Stable Lightweight Model for Metal Crack Detection Based on YOLOv5,
ICIVC22(123-128)
IEEE DOI 2301
Visualization, Cross layer design, Computational modeling, Metals, Object detection, Manuals, Inspection, deep learning, YOLOv5, metal crack detection BibRef

Zhang, Y.[Yu], Gao, Y.[Yan], Shen, L.Y.[Li-Yong],
Steel Defect Detection Based on Modified RetinaNet,
ICPR22(3572-3579)
IEEE DOI 2212
Semantics, Detectors, Network architecture, Feature extraction, Steel, Data mining, Task analysis BibRef

Lin, D.Y.[Dong-Yun], Cheng, Y.[Yi], Li, Y.Q.[Yi-Qun], Prasad, S.[Shitala], Guo, A.[Aiyuan],
MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation,
ICIP22(441-445)
IEEE DOI 2211
Training, Image segmentation, Product design, Decoding, Quality assessment, Task analysis, Defect Segmentation, UNet, Multi-Level Spatial Attention BibRef

Es-Sarraj, H.[Hamza], El Bakri, A.[Ayoub], Alaoui, R.M.[Rim Mrani], Boumhidi, I.[Ismail],
FSMC for PWM rotor side converter in DFIG-based wind turbine system,
ISCV22(1-7)
IEEE DOI 2208
Fluctuations, Uncertainty, Wind speed, System performance, Pulse width modulation, Wind power generation, fuzzy sliding mode control BibRef

Hödel, M., Hoegner, L., Stilla, U.,
Review on Photogrammetric Surface Inspection In Automotive Production,
ISPRS21(B2-2021: 511-518).
DOI Link 2201
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Kopf, L.[Larissa], Tighe, R.[Rachael],
Thermographic identification of hidden corrosion,
IVCNZ21(1-6)
IEEE DOI 2201
Temperature sensors, Temperature measurement, Image segmentation, Histograms, Thresholding (Imaging), Corrosion, Steel BibRef

Nath, V.[Vikanksh], Chattopadhyay, C.[Chiranjoy],
S2D2Net: An Improved Approach for Robust Steel Surface Defects Diagnosis With Small Sample Learning,
ICIP21(1199-1203)
IEEE DOI 2201
Manufacturing processes, Image recognition, Training data, Production, Inspection, Feature extraction, Industry 4.0 BibRef

Nishiura, H.[Hiromi], Miyamoto, A.[Atsushi], Ito, A.[Akira], Suzuki, S.[Shogo], Fujii, K.[Kouhei], Morifuji, H.[Hiroshi], Takatsuka, H.[Hiroyuki],
Machine-learning-based Quality-level-estimation System for Inspecting Steel Microstructures,
MVA21(1-4)
DOI Link 2109
Training, Microscopy, Quality control, Manuals, Machine learning, Inspection BibRef

Fang, J.T.[Jun-Ting ], Tan, X.Y.[Xiao-Yang], Wang, Y.H.[Yu-Hui],
ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection,
ICPR21(423-430)
IEEE DOI 2105
Filtering, Quality control, Production, Detectors, Feature extraction, Robustness, Pattern recognition BibRef

Peghini, N.[Nicola], Zignoli, A.[Andrea], Gandolfi, D.[Davide], Rota, P.[Paolo], Bosetti, P.[Paolo],
Real-time Cross-dataset Quality Production Assessment in Industrial Laser Cutting Machines,
IML20(490-505).
Springer DOI 2103
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Huang, Y.J.[Yu-Jen], Huang, K.W.[Ko-Wei], Lee, S.H.[Shih-Hsiung],
Defect Detection of Stainless Steel Plates Using Deep Learning Technology,
HCAU20(289-301).
Springer DOI 2103
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Dementev, V.E.[Vitalii E.], Gaponova, M.A.[Maria A.], Suetin, M.R.[Marat R.], Streltzova, A.S.[Anastasia S.],
The Use of Machine Learning Methods to Detect Defects in Images of Metal Structures,
IMTA20(120-128).
Springer DOI 2103
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Katsamenis, I.[Iason], Protopapadakis, E.[Eftychios], Doulamis, A.[Anastasios], Doulamis, N.[Nikolaos], Voulodimos, A.[Athanasios],
Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation,
ISVC20(I:160-169).
Springer DOI 2103
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Dong, J.J.[Jiao-Jiao], Wang, J.W.[Jian-Wen],
Simulation Study on the Effect of Torque Load on the Temperature Distribution of TRP,
CVIDL20(453-456)
IEEE DOI 2102
computer simulation, finite element analysis, rolling bearings, temperature distribution, thermal analysis. BibRef

Dawda, A., Nguyen, M.,
Defects Detection in Highly Specular Surface using a Combination of Stereo and Laser Reconstruction,
IVCNZ20(1-6)
IEEE DOI 2012
Surface reconstruction, Lighting, Surface emitting lasers, Inspection, Image reconstruction, Inspection BibRef

Tabernik, D.[Domen], Šela, S.[Samo], Skvarc, J.[Jure], Skocaj, D.[Danijel],
Deep-learning-based Computer Vision System for Surface-defect Detection,
CVS19(490-500).
Springer DOI 1912
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Costantino, D., Pepe, M., Alfio, V., Carrieri, M.,
Geomatic Techniques for Monitoring and Verifying of The Wear Condition Of The Runways of The Bridge Cranes,
Optical3D19(23-30).
DOI Link 1912
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Jafari-Tabrizi, A.[Atae], Lichtenegger, H.L.[Hannah Luise], Gruber, D.P.[Dieter P.],
A Method for the Evaluation and Classification of the Orange Peel Effect on Painted Injection Moulded Part Surfaces,
IbPRIA19(II:453-464).
Springer DOI 1910
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Fitti, M.[Matteo], Castellini, P.[Paolo], Paone, N.[Nicola], Zannini, M.[Marco], Zitti, S.[Saverio], Gambini, M.[Marco], Chiariotti, P.[Paolo],
In-Line Burr Inspection Through Backlight Vision,
NTIAP19(343-351).
Springer DOI 1909
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Han, X.[Xu], Yu, H.R.[Hao-Ran], Gu, H.S.[Hai-Song],
Visual Inspection with Federated Learning,
ICIAR19(II:52-64).
Springer DOI 1909
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Tandiya, A., Akthar, S., Moussa, M., Tarray, C.,
Automotive Semi-specular Surface Defect Detection System,
CRV18(285-291)
IEEE DOI 1812
Cameras, Inspection, Surface topography, Surface reconstruction, Robots, Surface treatment, Image edge detection, Defect detection, painted surface inspection BibRef

Lech, P.[Piotr], Fastowicz, J.[Jaroslaw], Okarma, K.[Krzysztof],
Quality Evaluation of 3D Printed Surfaces Based on HOG Features,
ICCVG18(199-208).
Springer DOI 1810
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de Mesquita Sá, Jr., J.J.[Jarbas Joaci], Backes, A.R.[André R.], Bruno, O.M.[Odemir Martinez],
Randomized Neural Network Based Signature for Classification of Titanium Alloy Microstructures,
CIARP17(669-676).
Springer DOI 1802

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Chai, W.H., Ho, S.S., Goh, C.K., Chia, L.T., Quek, H.C.,
A fast sparse reconstruction approach for high resolution image-based object surface anomaly detection,
MVA17(13-16)
DOI Link 1708
Image reconstruction, Image resolution, Inspection, Lighting, Optimization, Upper bound, Visualization BibRef

Mery, D.[Domingo], Arteta, C.,
Automatic Defect Recognition in X-Ray Testing Using Computer Vision,
WACV17(1026-1035)
IEEE DOI 1609
Casting, Databases, Feature extraction, Solid modeling, Testing, X-ray, imaging BibRef

Pitard, G.[Gilles], Le Goïc, G.[Gaëtan], Mansouri, A.[Alamin], Favrelière, H.[Hugues], Pillet, M.[Maurice], George, S.[Sony], Hardeberg, J.Y.[Jon Yngve],
Robust Anomaly Detection Using Reflectance Transformation Imaging for Surface Quality Inspection,
SCIA17(I: 550-561).
Springer DOI 1706
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Starostenko, O.[Oleg], Trygub, I.G.[Irina G.], Cruz-Perez, C.[Claudia], Alarcon-Aquino, V.[Vicente], Potap, O.E.[Oleg E.],
Visual Remote Monitoring and Control System for Rod Braking on Hot Rolling Mills,
MCPR17(297-307).
Springer DOI 1706
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Eseholi, T., Notta-Cuvier, D., Coudoux, F.X., Corlay, P., Robache, F., Bigerelle, M.,
Performance evaluation of strain field measurement by digital image correlation using HEVC compressed ultra-high speed video sequences,
ISIVC16(142-147)
IEEE DOI 1704
Encoding BibRef

Hung, T.Y.[Tzu-Yi], Vaikundam, S.[Sriram], Natarajan, V.[Vidhya], Chia, L.T.[Liang-Tien],
Phase Fourier Reconstruction for Anomaly Detection on Metal Surface Using Salient Irregularity,
MMMod17(I: 290-302).
Springer DOI 1701
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Vaikundam, S., Hung, T.Y., Chia, L.T.,
Anomaly region detection and localization in metal surface inspection,
ICIP16(759-763)
IEEE DOI 1610
Decision support systems BibRef

Watcharopas, C.[Chakrit], Sapra, Y.[Yash], Geist, R.[Robert], Levine, J.A.[Joshua A.],
Extracting Surface Geometry from Particle-Based Fracture Simulations,
ISVC15(I: 82-91).
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Balle, F.[Frank], Eifler, D.[Dietmar], Fitschen, J.H.[Jan Henrik], Schuff, S.[Sebastian], Steidl, G.[Gabriele],
Computation and Visualization of Local Deformation for Multiphase Metallic Materials by Infimal Convolution of TV-Type Functionals,
SSVM15(385-396).
Springer DOI 1506
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Florian, D., Sonnleithner, L., Zagar, B.G.,
Determining copper surface change ratio of conduction path by using image processing,
IPTA14(1-6)
IEEE DOI 1503
copper BibRef

Yamashita, N.[Norio], Yoshizawa, S.[Shin], Yokota, H.[Hideo],
Volume-based shape analysis for internal microstructure of steels,
ICIP14(4887-4891)
IEEE DOI 1502
Fatigue BibRef

Soukup, D., Huber-Mörk, R.,
Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images,
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MacDonald, L., Hindmarch, J., Robson, S., Terras, M.,
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CloseRange14(371-377).
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Freye, C.[Christian], Bendicks, C.[Christian], Lilienblum, E.[Erik], Al-Hamadi, A.[Ayoub],
Multiple Camera Approach for SLAM Based Ultrasonic Tank Roof Inspection,
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Zhao, F.[Fei], Mendonca, P.R.S.[Paulo R.S.], Yu, J.[Jie], Kaucic, R.[Robert],
Learning-based automatic defect recognition with computed tomographic imaging,
ICIP13(2762-2766)
IEEE DOI 1412
ADR;Aluminum Casting Defects;CT BibRef

Ducato, A.[Antonino], Fratini, L.[Livan], Cascia, M.L.[Marco La],
An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy,
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Juan, C., William, C., David, M., George, A., Delgado, G., Vladimir, D.,
Automatic ship hull inspection using fuzzy logic,
AIPR12(1-5)
IEEE DOI 1307
fuzzy logic BibRef

Radu-Eugen, B.[Breaz], Sorin, T.[Tarnovean], Cristina, B.[Biris], Octavian-Constantin, B.[Bologa],
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IEEE DOI 1304
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Sato, R.[Ryoichi], Kato, K.[Kunihito], Harada, K.[Kouta],
Development of the hole position inspection system of pressed car parts by using laser 3-d measurement,
FCV13(317-322).
IEEE DOI 1304
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Vigneswaran, C., Madhu, M., Rajamani, R.,
Inspection and error analysis of Geneva gear on machine vision system using Sherlock™ and VB 6.0 Algorithm,
IMVIP12(193-196).
IEEE DOI 1302
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Gopan, V.[Vipin], Ragavanantham, S., Sampathkumar, S.,
Condition monitoring of grinding process through machine vision system,
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IEEE DOI 1302
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Spatter Tracking in Laser Machining,
ISVC12(II: 626-635).
Springer DOI 1209
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Avsar, E.O., Altan, M.O., Dogan, U.A., Akca, D.,
Determining Pull-Out Deformations of Bonded Metal Anchors Embedded In Concrete by Means of Photogrammetry,
ISPRS12(XXXIX-B5:5-8).
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IASP11(155-158).
IEEE DOI 1112
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Tang, X.N.[Xiang-Na], Wang, Y.N.[Yao-Nan],
Visual inspection of workpiece quality,
IASP11(434-438).
IEEE DOI 1112
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Fingerprints for Machines: Characterization and Optical Identification of Grinding Imprints,
DAGM11(276-285).
Springer DOI 1109
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Belton, D.[David], Gibson, A.[Aaron], Stansby, B.[Bruce], Tingay, S.[Steven], Bae, K.H.[Kwang-Ho],
Modelling of an Inexpensive 9M Satellite Dish from 3D Point Clouds Captured By Terrestrial Laser Scanners,
Laser11(xx-yy).
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Detailed shape analysis -- is it a parabola, or how has it changed. BibRef

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Robotic Tracking and Marking of Surface Shape Defects on Moving Automotive Panels,
CRV11(56-63).
IEEE DOI 1105
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Kaviti, A.K.[Ajay Kumar], Pathak, K.K., Hora, M.S.,
Application of Neural Networks in Preform Design of Aluminium Upsetting Process Considering Different Interfacial Frictional Conditions,
PReMI09(98-105).
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Liu, W.L.[Wei-Ling], Liu, L.B.[Li-Bing], Zhang, H.M.[Hong-Mei], Yang, Z.Q.[Ze-Qing],
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CISP09(1-3).
IEEE DOI 0910
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Li, H.[Hui], Fu, L.H.[Li-Hui], Li, Z.[Zhentao],
Gear Fault Detection Using Angle Domain Average and Hilbert-Huang Transform Phase Map,
CISP09(1-5).
IEEE DOI 0910
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Fu, L.H.[Li-Hui], Li, H.[Hui],
Gear Fault Diagnosis Based on Order Tracking and Degree of Cyclostationarity,
CISP09(1-5).
IEEE DOI 0910
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Li, L.[Li], Li, B.[Bing], Shen, Y.L.[Yi-Lin],
Electric Heating Cable Fault Testing System Based on Wavelet Packet and RBF Neural Network,
CISP09(1-4).
IEEE DOI 0910
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Marot, J.[Julien], Bourennane, S.[Salah], Spinnler, K.[Klaus],
Metal surface control system based on successive contour estimation,
ICIP10(2293-2296).
IEEE DOI 1009
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Mumtaz, M.[Mustafa], Mansoor, A.B.[Atif Bin], Masood, H.[Hassan],
A New Approach to Aircraft Surface Inspection Based on Directional Energies of Texture,
ICPR10(4404-4407).
IEEE DOI 1008
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Cefalu, A., Böhm, J.,
Development Of A Robot Guided Optical Multisensory System For Inline Inspection Of Cylinder Heads,
CloseRange10(xx-yy).
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Rauch, L.[Lukasz], Madej, L.[Lukasz], Pawlowski, B.[Bogdan],
Numerical Simulations of Hypoeutectoid Steels under Loading Conditions, Based on Image Processing and Digital Material Representation,
CompIMAGE10(221-230).
Springer DOI 1006
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Alegre, E.[Enrique], Alaiz-Rodríguez, R.[Rocío], Barreiro, J.[Joaquín], Fidalgo, E.[Eduardo], Fernández, L.[Laura],
Surface Finish Control in Machining Processes Using Haralick Descriptors and Neuronal Networks,
CompIMAGE10(231-241).
Springer DOI 1006
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Savran, Y.[Yigitcan], Gunsel, B.[Bilge],
Novelty Detection on Metallic Surfaces by GMM Learning in Gabor Space,
ICIAR10(II: 325-334).
Springer DOI 1006
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Germani, M.[Michele], Mengoni, M.[Maura], Raffaeli, R.[Roberto],
Automation of 3D view acquisition for geometric tolerances verification,
3DIM09(1710-1717).
IEEE DOI 0910
Inspection of mechanical components. BibRef

Jaramillo, A.E.[Andres E.], Boulanger, P.[Pierre], Prieto, F.[Flavio],
On-line 3-D inspection of deformable parts using FEM trained radial basis functions,
3DIM09(1733-1739).
IEEE DOI 0910
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Gallego-Sanchez, J.[Javier], Calera-Rubio, J.[Jorge],
Improving edge detection in highly noised sheet-metal images,
WACV09(1-6).
IEEE DOI 0912
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Usamentiaga, R.[Rubén], Molleda, J.[Julio], García, D.F.[Daniel F.], Bulnes, F.G.[Francisco G.],
Machine Vision System for Flatness Control Feedback,
ICMV09(105-110).
IEEE DOI 0912
metal quality control. BibRef

Polišcuk, R.[Radek],
Image Processing Methods Applied in Mapping of Lubrication Parameters,
ISVC09(II: 1011-1020).
Springer DOI 0911
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Silva, J.[Jayant], Dana, K.J.[Kristin J.],
Color Matching for Metallic Coatings,
ISVC09(II: 335-344).
Springer DOI 0911
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Sun, Z.Y.[Zhao-Yun], Wang, C.F.[Chao-Fan], Sha, A.M.[Ai-Min], Chen, K.[Kai],
Image-Based Molding Effect Analysis of HWTD Sample,
CISP09(1-4).
IEEE DOI 0910
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Min, Y.[Yang], Li, M.[Mou],
Striation Patterns Classification of Tool Marks Based on Morphological Structure Features,
CISP09(1-5).
IEEE DOI 0910
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Li, S.K.[Shu-Kui], Nie, S.M.[Shao-Min],
Image Segmentation Method of Heavy Forgings Based on Genetic Algorithm,
CISP09(1-4).
IEEE DOI 0910
BibRef

Li, X.C.[Xin-Cheng], Cai, W.[Wang], Zhu, W.X.[Wei-Xing], Zhang, Z.Y.[Zhao-Yang], Xun, B.[Bin],
The Research on Measuring Analysis and Prediction of the Nanoscale Precipitated Phase in Steel,
CISP09(1-4).
IEEE DOI 0910
BibRef

Ma, H.[Hui], Sun, W.[Wei], Ren, Z.H.[Zhao-Hui], Wen, B.C.[Bang-Chun],
Feature Extraction of Rotor Systems with Coupling Fault with Crack and Rub-Impact,
CISP09(1-5).
IEEE DOI 0910
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Li, X.C.[Xin-Cheng], Ding, F.[Fei], Zhang, Z.Y.[Zhao-Yang], Zhu, W.X.[Wei-Xing], Liu, D.[Dan],
Ultra-Fine Grain Steel's Metallurgical Image Restoration Method Based on Improved Water-Growing,
CISP09(1-4).
IEEE DOI 0910
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Hong, Z.[Zhao],
Research on Automatic Inspection and Classification for Middle Thickness Pb Alloy Castings Based on Machine Vision Recognition Technology,
CISP09(1-4).
IEEE DOI 0910
BibRef

Li, X.C.[Xin-Cheng], Wang, X.L.[Xin-Liang], Zhu, W.X.[Wei-Xing], Zhang, Z.Y.[Zhao-Yang],
Research on Measurement and Classification System Design and Application about Ultra Steel Dimple,
CISP09(1-4).
IEEE DOI 0910
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Feng, Z.P.[Zhi-Peng], Hao, R.J.[Ru-Jiang], Chu, F.L.[Fu-Lei],
Application of Cyclic Correlation Analysis to Gearbox Damage Assessment,
CISP09(1-5).
IEEE DOI 0910
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Wang, Z.Q.[Zhi-Qiang], Xu, Y.X.[Yu-Xiu], Mei, Y.Y.[Yuan-Ying],
Damage Diagnosis for Wind Turbine Blades Based on the Shifting Distance of Characteristic Frequency,
CISP09(1-3).
IEEE DOI 0910
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Zhang, H.G.[Hai-Guang], Hu, Q.X.[Qing-Xi], Liu, Y.Y.[Yuan-Yuan], Wang, J.W.[Jia-Wei], Chi, Z.F.[Zhao-Fu], Huang, J.[Jie],
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Inspection of Stamped Sheet Metal Car Parts Using a Multiresolution Image Fusion Technique,
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Springer DOI 0910
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Dragon, R.[Ralf], Becker, C.[Christian], Rosenhahn, B.[Bodo], Ostermann, J.[Jörn],
Reading from Scratch: A Vision-System for Reading Data on Micro-structured Surfaces,
DAGM09(402-411).
Springer DOI 0909
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Chuang, H.C.A.[Hsiao-Chi-Ang], Huffman, L.M.[Landis M.], Comer, M.L.[Mary L.], Simmons, J.P.[Jeff P.], Pollak, I.[Ilya],
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IEEE DOI 0810
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Huber-Mörk, R., Zaharieva, M., Czedik-Eysenberg, H.,
Numismatic Object Identification Using Fusion of Shape and Local Descriptors,
ISVC08(II: 368-379).
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Amini, A.S., Varshosaz, M., Saadatseresht, M.,
Deformation Determination of Aircraft Parts by Photogrammetry,
ISPRS08(B5: 135 ff).
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Jung, S.H., Yu, J.H., Ge, L., Lee, J.K.,
Automatic Modelling Method for Steel Structures Using Photogrammetry,
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Alegre, E.[Enrique], Barreiro, J.[Joaquín], Castejón, M.[Manuel], Suarez, S.[Sir],
Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes,
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Wu, P.H.[Ping-Han], Li, Y.W.[Yu-Wei], Chu, C.H.[Chih-Hsing],
Tool Path Planning for 5-Axis Flank Milling Based on Dynamic Programming Techniques,
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Zhang, X.[Xi], Tian, X.D.[Xiao-Dong], Yamazaki, K.[Kazuo], Fujishima, M.[Makoto],
3D Reconstruction and Pose Determination of the Cutting Tool from a Single View,
ISVC07(II: 377-386).
Springer DOI 0711
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da Silva, R.R.[Romeu Ricardo], Mery, D.[Domingo],
Accuracy Estimation of Detection of Casting Defects in X-Ray Images Using Some Statistical Techniques,
PSIVT07(639-650).
Springer DOI 0712
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Benesova, W., Rinnhofer, A., Jakob, G.,
Determining the Average Grain Size of Super-Alloy Micrographs,
ICIP06(2749-2752).
IEEE DOI 0610
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Rinnhofer, A.[Alfred], Benesova, W.[Wanda], Jakob, G.[Gerhard], Stockinger, M.[Manfred],
Feature Extraction from Micrographs of Forged Nickel Based Alloy,
ICPR06(II: 391-394).
IEEE DOI 0609
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Gayubo, F.[Fernando], Gonzalez, J.L.[Jose L.], de la Fuente, E.[Eusebio], Miguel, F.[Félix], Peran, J.R.[Jose R.],
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ICPR06(I: 723-726).
IEEE DOI 0609
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Claes, K., Koninckx, T.P., Bruyninckx, H.,
Automatic burr detection on surfaces of revolution based on adaptive 3D scanning,
3DIM05(212-219).
IEEE DOI 0508
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Reindl, I.[Ingo], O'Leary, P.[Paul],
Geometric Surface Inspection of Raw Milled Steel Blocks,
ICIAR04(II: 849-856).
Springer DOI 0409
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Hernández, S.[Sergio], Sáez, D.[Doris], Mery, D.[Domingo],
Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings,
ICIAR04(II: 826-833).
Springer DOI 0409
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Mery, D.[Domingo],
Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Casting,
SCIA03(725-732).
Springer DOI 0310
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Varma, S., Baras, I.S.,
Tool wear estimation from acoustic emissions: A model incorporating wear-rate,
ICPR02(I: 492-495).
IEEE DOI 0211
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González, R.C.[Rafael C.], Valdés, R.[Raul], Cancelas, J.A.[Jose A.],
Vision Based Measurement System to Quantify Straightness Defect in Steel Sheets,
CAIP01(427 ff.).
Springer DOI 0210
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Wade, P.[Paul], Moran, D.[David], Graham, J.[Jim], Jackson, C.B.[C. Brook],
Robust and Accurate 3D Measurement of Formed Tube Using Trinocular Stereo Vision,
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Mian, Z., Kassim, A.A., Mannan, M.A.,
Texture Anaysis of Machined Surfaces using a new Hough Transform,
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Zhou, J., Xin, L.P., Gao, D.S., Zhang, C.S., Zhang, D.,
Automated Cartridge Identification for Firearm Authentication,
CVPR01(I:749-754).
IEEE DOI 0110
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Robertson, C., Fisher, R.B.,
Shape Recovery and Analysis of Large Screw Threads,
3DIM01(300-305).
IEEE DOI BibRef 0100 Edinburgh 0106
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Derganc, J., Pernus, F.,
A Machine Vision System for Inspecting Bearings,
ICPR00(Vol IV: 752-755).
IEEE DOI 0009
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Lilienblum, T., Albrecht, P., Calow, R., Michaelis, B.,
Dent Detection in Car Bodies,
ICPR00(Vol IV: 775-778).
IEEE DOI 0009
Structured light. BibRef

Kyrki, V., Kälviäinen, H.,
High Precision 2-d Geometrical Inspection,
ICPR00(Vol IV: 779-782).
IEEE DOI 0009
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Chivers, K., Clocksin, W.,
Inspection of Surface Strain in Materials using Optical Flow,
BMVC00(xx-yy).
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Komenda, J.,
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SSAB97(Image Processing) 9703
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Ancis, M., Campus, A., Giusto, D.D., Tintrup, F.,
XR-US Data Fusion for Non-destructive Evaluation of Steel Components,
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Classification of corrosion images by wavelet signatures and LVQ networks,
CAIP95(538-543).
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Dinten, J.M., Dziopa, P., Koenig, A.,
X-rays image analysis for defects detection and characterization in metallic samples,
ICIP94(II: 321-325).
IEEE DOI 9411
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Dias, A.F., de Albuquerque Araújo, A.[Arnaldo], Crispim, V.R.,
Aluminum corrosion detection by using a neutron radiographic image analyzer,
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Tuzikov, A., Soille, P., Jeulin, D., Bruneel, H., Vermeulen, M.,
Extraction of Grid Patterns on Stamped Metal Sheets Using Mathematical Morphology,
ICPR92(I:425-428).
IEEE DOI BibRef 9200

Maliniemi, H., Ailisto, H., Hakalahti, H.,
Vision system for turbine inspection,
ICPR88(II: 1202-1206).
IEEE DOI 8811
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Zavidovique, B., Merlo, X., and Foulloy, L.,
Hey Robot... Looking for Cones?,
CVPR85(379-381). (ADERP/ECTA) To find conical metal shapes, real time implementation. BibRef 8500

Mundy, J.L., Porter, III, G.B.,
Visual Inspection of Metal Surfaces,
ICPR80(232-237). BibRef 8000

Horaud, R., Charras, J.P.,
Automatic Inspection and Orientation of External Screws,
ICPR80(264-268). BibRef 8000

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Metal, Coins .