19.6.3.9.1 Inspection -- Pavement, Road Surface, Asphalt, Concrete

Chapter Contents (Back)
Pavement Analysis. Concrete Inspection. Crack Detection. Application, Inspection. Inspection, Defects. Defect Detection. Bridges, deformation, structural: See also Deformation of Bridges, Monitor Bridges, Other Structures.

Kalliomäki, I.[Ilkka], Vehtari, A.[Aki], Lampinen, J.[Jouko],
Shape analysis of concrete aggregates for statistical quality modeling,
MVA(16), No. 3, May 2005, pp. 197-201.
Springer DOI 0505
BibRef

Le Bastard, C., Baltazart, V., Wang, Y., Saillard, J.,
Thin-Pavement Thickness Estimation Using GPR With High-Resolution and Superresolution Methods,
GeoRS(45), No. 8, August 2007, pp. 2511-2519.
IEEE DOI 0709
BibRef

Bourlier, C., Le Bastard, C., Baltazart, V.,
Generalization of PILE Method to the EM Scattering From Stratified Subsurface With Rough Interlayers: Application to the Detection of Debondings Within Pavement Structure,
GeoRS(53), No. 7, July 2015, pp. 4104-4115.
IEEE DOI 1503
Ground penetrating radar BibRef

Yamaguchi, T.[Tomoyuki], Hashimoto, S.[Shuji],
Fast crack detection method for large-size concrete surface images using percolation-based image processing,
MVA(21), No. 5, August 2010, pp. 797-809.
WWW Link. 1011
BibRef
Earlier:
Improved percolation-based method for crack detection in concrete surface images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Clemmensen, L.H.[Line H.], Hansen, M.E.[Michael E.], Ersbřll, B.K.[Bjarne K.],
A comparison of dimension reduction methods with application to multi-spectral images of sand used in concrete,
MVA(21), No. 6, October 2010, pp. 959-968.
WWW Link. 1011
BibRef

Fujita, Y.[Yusuke], Hamamoto, Y.[Yoshihiko],
A robust automatic crack detection method from noisy concrete surfaces,
MVA(22), No. 2, March 2011, pp. 245-254.
WWW Link. 1103
BibRef

Elunai, R., Chandran, V., Gallagher, E.,
Asphalt Concrete Surfaces Macrotexture Determination From Still Images,
ITS(12), No. 3, September 2011, pp. 857-869.
IEEE DOI 1109
BibRef

Li, Q.Q.[Qing-Quan], Zou, Q.[Qin], Zhang, D.Q.[Da-Qiang], Mao, Q.Z.[Qing-Zhou],
FoSA: F* Seed-growing Approach for crack-line detection from pavement images,
IVC(29), No. 12, November 2011, pp. 861-872.
Elsevier DOI 1112
Line detection; Pavement crack; Seed-growing; Dynamic programming BibRef

Zou, Q.[Qin], Cao, Y.[Yu], Li, Q.Q.[Qing-Quan], Mao, Q.Z.[Qing-Zhou], Wang, S.[Song],
CrackTree: Automatic crack detection from pavement images,
PRL(33), No. 3, 1 February 2012, pp. 227-238.
Elsevier DOI 1201
Crack detection; Edge detection; Edge grouping; Tensor voting; Shadow removal BibRef

Suanpaga, W., Yoshikazu, K.,
Riding Quality Model for Asphalt Pavement Monitoring Using Phase Array Type L-band Synthetic Aperture Radar (PALSAR),
RS(2), No. 11, November 2010, pp. 2531-2546.
DOI Link 1203
BibRef

Ndoye, M., Barker, A.M., Krogmeier, J.V., Bullock, D.M.,
A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road-Condition-Monitoring System,
ITS(12), No. 3, September 2011, pp. 795-808.
IEEE DOI 1109
BibRef

Oliveira, H., Correia, P.L.,
Automatic Road Crack Detection and Characterization,
ITS(14), No. 1, March 2013, pp. 155-168.
IEEE DOI 1303
BibRef

Fang, H., Lin, G., Zhang, R.,
The First-Order Symplectic Euler Method for Simulation of GPR Wave Propagation in Pavement Structure,
GeoRS(51), No. 1, January 2013, pp. 93-98.
IEEE DOI 1301
BibRef

Shangguan, P.C.[Peng-Cheng], Al-Qadi, I.L.,
Calibration of FDTD Simulation of GPR Signal for Asphalt Pavement Compaction Monitoring,
GeoRS(53), No. 3, March 2015, pp. 1538-1548.
IEEE DOI 1412
asphalt BibRef

Guan, H.[Haiyan], Li, J., Yu, Y.T.[Yong-Tao], Chapman, M., Wang, H.[Hanyun], Wang, C.[Cheng], Zhai, R.F.[Rui-Fang],
Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data,
GeoRS(53), No. 3, March 2015, pp. 1527-1537.
IEEE DOI 1412
crack detection See also Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data. BibRef

Yi, C., Chuang, Y., Nian, C.,
Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies,
ITS(16), No. 4, August 2015, pp. 1905-1917.
IEEE DOI 1508
Feature extraction BibRef

Rajamohan, D.[Deepak], Gannu, B.[Bhavana], Rajan, K.S.[Krishnan Sundara],
MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data,
IJGI(4), No. 3, 2015, pp. 1225.
DOI Link 1508
BibRef

Mathavan, S., Kamal, K., Rahman, M.,
A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements,
ITS(16), No. 5, October 2015, pp. 2353-2362.
IEEE DOI 1511
Survey, Pavement Analysis. computer vision BibRef

Quintana, M., Torres, J., Menéndez, J.M.,
A Simplified Computer Vision System for Road Surface Inspection and Maintenance,
ITS(17), No. 3, March 2016, pp. 608-619.
IEEE DOI 1603
Cameras BibRef

Zhang, S.[Su], Lippitt, C.D.[Christopher D.], Bogus, S.M.[Susan M.], Neville, P.R.H.[Paul R. H.],
Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography,
RS(8), No. 5, 2016, pp. 392.
DOI Link 1606
BibRef

Hoult, N.A., Dutton, M., Hoag, A., Take, W.A.,
Measuring Crack Movement in Reinforced Concrete Using Digital Image Correlation: Overview and Application to Shear Slip Measurements,
PIEEE(104), No. 8, August 2016, pp. 1561-1574.
IEEE DOI 1608
Area measurement BibRef

Amhaz, R.[Rabih], Chambon, S.[Sylvie], Idier, J.[Jerome], Baltazart, V.[Vincent],
Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection,
ITS(17), No. 10, October 2016, pp. 2718-2729.
IEEE DOI 1610
BibRef
Earlier:
A new minimal path selection algorithm for automatic crack detection on pavement images,
ICIP14(788-792)
IEEE DOI 1502
Context Cost function BibRef

Ishikawa, T.[Tsuyoshi], Fujinami, K.[Kaori],
Smartphone-Based Pedestrian's Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection,
IJGI(5), No. 10, 2016, pp. 182.
DOI Link 1610
BibRef

Jang, D.W., Park, R.H.,
Pothole detection using spatio-temporal saliency,
IET-ITS(10), No. 9, 2016, pp. 605-612.
DOI Link 1609
asphalt BibRef

Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.,
Automatic Road Crack Detection Using Random Structured Forests,
ITS(17), No. 12, December 2016, pp. 3434-3445.
IEEE DOI 1612
Feature extraction BibRef

Zaini, N.[Nasrullah], van der Meer, F.[Freek], van Ruitenbeek, F.[Frank], de Smeth, B.[Boudewijn], Amri, F.[Fadli], Lievens, C.[Caroline],
An Alternative Quality Control Technique for Mineral Chemistry Analysis of Portland Cement-Grade Limestone Using Shortwave Infrared Spectroscopy,
RS(8), No. 11, 2016, pp. 950.
DOI Link 1612
BibRef

Zhang, D.[Dejin], Li, Q.Q.[Qing-Quan], Chen, Y.[Ying], Cao, M.[Min], He, L.[Li], Zhang, B.L.[Bai-Ling],
An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection,
IVC(57), No. 1, 2017, pp. 130-146.
Elsevier DOI 1702
Pavement crack detection BibRef

Casselgren, J.[Johan], Bodin, U.[Ulf],
Reusable road condition information system for traffic safety and targeted maintenance,
IET-ITS(11), No. 4, May 2017, pp. 230-238.
DOI Link 1705
BibRef

Lenglet, C.[Céline], Blanc, J.[Juliette], Dubroca, S.[Stéphane],
Smart road that warns its network manager when it begins cracking,
IET-ITS(11), No. 3, April 2017, pp. 152-157.
DOI Link 1705
BibRef

González, L.C., Moreno, R., Escalante, H.J., Martínez, F., Carlos, M.R.,
Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation,
ITS(18), No. 11, November 2017, pp. 2916-2928.
IEEE DOI 1711
Accelerometers, Automobiles, Data collection, Roads, Sensors, Smart phones, Urban areas, Roadway surface disruptions, accelerometer. BibRef

Carmon, N.[Nimrod], Ben-Dor, E.[Eyal],
Mapping Asphaltic Roads' Skid Resistance Using Imaging Spectroscopy,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Carlos, M.R., Aragón, M.E., González, L.C., Escalante, H.J., Martínez, F.,
Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings: Addressing Who's Who,
ITS(19), No. 10, October 2018, pp. 3334-3343.
IEEE DOI 1810
Roads, Accelerometers, Sensors, Support vector machines, Proposals, Acceleration, Accelerometer measurements, mobile sensing, road anomalies BibRef

Zhang, Y., Chen, C., Wu, Q., Lu, Q., Zhang, S., Zhang, G., Yang, Y.,
A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition,
ITS(19), No. 12, December 2018, pp. 3935-3946.
IEEE DOI 1812
Surface cracks, Surface reconstruction, Sensors, Image reconstruction, BibRef

Zhao, S., Al-Qadi, I.L.,
Super-Resolution of 3-D GPR Signals to Estimate Thin Asphalt Overlay Thickness Using the XCMP Method,
GeoRS(57), No. 2, February 2019, pp. 893-901.
IEEE DOI 1901
Signal resolution, Ground penetrating radar, Dielectric constant, Antennas, Image resolution, Asphalt, Estimation, thin asphalt overlay BibRef

Li, Z.Q.[Zhi-Qiang], Cheng, C.Q.[Cheng-Qi], Kwan, M.P.[Mei-Po], Tong, X.C.[Xiao-Chong], Tian, S.H.[Shao-Hong],
RETRACTION: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
BibRef
And: IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Feng, H.[Hui], Xu, G.S.[Guo-Sheng], Guo, Y.H.[Yan-Hui],
Multi-scale classification network for road crack detection,
IET-ITS(13), No. 2, February 2019, pp. 398-405.
DOI Link 1902
BibRef

Li, H., Song, D., Liu, Y., Li, B.,
Automatic Pavement Crack Detection by Multi-Scale Image Fusion,
ITS(20), No. 6, June 2019, pp. 2025-2036.
IEEE DOI 1906
Training data, Manuals, Feature extraction, Training, Clutter, Image edge detection, Fuses, Crack detection, robotic airport runway inspection BibRef

Kaddah, W.[Wissam], Elbouz, M.[Marwa], Ouerhani, Y.[Yousri], Baltazart, V.[Vincent], Alfalou, A.[Ayman],
Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images,
VC(35), No. 9, September 2018, pp. 1293-1309.
Springer DOI 1908
BibRef

Tan, Y.M.[Yu-Min], Li, Y.X.[Yun-Xin],
UAV Photogrammetry-Based 3D Road Distress Detection,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Fan, R., Ozgunalp, U., Hosking, B., Liu, M., Pitas, I.,
Pothole Detection Based on Disparity Transformation and Road Surface Modeling,
IP(29), No. 1, 2020, pp. 897-908.
IEEE DOI 1910
BibRef
And: Corrections: IP(29), 2020, pp. 3091-3091.
IEEE DOI 2002
Pothole detection, road surface modeling. Roads, Surface treatment, Surface reconstruction, Detection algorithms, Sea surface, surface normal BibRef

Yang, W.W.[Wen-Wei],
Finite element model of concrete material based on CT image processing technology,
JVCIR(64), 2019, pp. 102631.
Elsevier DOI 1911
CT image, Numerical model, Concrete, Failure process BibRef

Cheng, L.[Lushan], Zhang, X.[Xu], Shen, J.[Jie],
Road surface condition classification using deep learning,
JVCIR(64), 2019, pp. 102638.
Elsevier DOI 1911
Deep learning, Road condition, Activation function, Image recognition, Intelligent driving BibRef

Hadavandsiri, Z.[Zahra], Lichti, D.D.[Derek D.], Jahraus, A.[Adam], Jarron, D.[David],
Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Mohammadi, M.E.[Mohammad Ebrahim], Wood, R.L.[Richard L.], Wittich, C.E.[Christine E.],
Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Fei, Y., Wang, K.C.P., Zhang, A., Chen, C., Li, J.Q., Liu, Y., Yang, G., Li, B.,
Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V,
ITS(21), No. 1, January 2020, pp. 273-284.
IEEE DOI 2001
Surface cracks, Asphalt, Libraries, Feature extraction, Deep learning, Kernel, CrackNet, CrackNet-V, surface cracks BibRef

De Blasiis, M.R.[Maria Rosaria], Di Benedetto, A.[Alessandro], Fiani, M.[Margherita],
Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Kashiyama, T.[Takehiro], Sekimoto, Y.[Yoshihide], Seto, T.[Toshikazu], Lwin, K.K.[Ko Ko],
Analyzing Road Coverage of Public Vehicles According to Number and Time Period for Installation of Road Inspection Systems,
IJGI(9), No. 3, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., Ling, H.,
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection,
ITS(21), No. 4, April 2020, pp. 1525-1535.
IEEE DOI 2004
Feature extraction, Image edge detection, Deep learning, Boosting, Task analysis, Semantics, Wavelet transforms, hierarchical boosting BibRef

Pu, Z.[Ziyuan], Cui, Z.Y.[Zhi-Yong], Wang, S.[Shuo], Li, Q.[Qianmu], Wang, Y.[Yinhai],
Time-aware gated recurrent unit networks for forecasting road surface friction using historical data with missing values,
IET-ITS(14), No. 4, April 2020, pp. 213-219.
DOI Link 2004
BibRef

Kaddah, W.[Wissam], Elbouz, M.[Marwa], Ouerhani, Y.[Yousri], Alfalou, A.[Ayman], Desthieux, M.[Marc],
Automatic darkest filament detection (ADFD): a new algorithm for crack extraction on two-dimensional pavement images,
VC(36), No. 7, July 2020, pp. 1369-1384.
Springer DOI 2005
BibRef

Meyer, F.J.[Franz J.], Ajadi, O.A.[Olaniyi A.], Hoppe, E.J.[Edward J.],
Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Xiang, X.Z.[Xue-Zhi], Zhang, Y.Q.[Yu-Qi], El Saddik, A.[Abdulmotaleb],
Pavement crack detection network based on pyramid structure and attention mechanism,
IET-IPR(14), No. 8, 19 June 2020, pp. 1580-1586.
DOI Link 2005
BibRef

Zou, L., Yi, L., Sato, M.,
On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System,
GeoRS(58), No. 6, June 2020, pp. 4215-4224.
IEEE DOI 2005
Asphalt airport pavement, common midpoint (CMP), ground-penetrating radar (GPR), interlayer debonding detection, nondestructive inspection BibRef

Du, Y., Liu, C., Song, Y., Li, Y., Shen, Y.,
Rapid Estimation of Road Friction for Anti-Skid Autonomous Driving,
ITS(21), No. 6, June 2020, pp. 2461-2470.
IEEE DOI 2006
Roads, Friction, Resistance, Electrical resistance measurement, Standards, Autonomous vehicles, Immune system, Autonomous vehicle, velocity control BibRef

Rodés, J.P.[Josep Pedret], Reguero, A.M.[Adriana Martínez], Pérez-Gracia, V.[Vega],
GPR Spectra for Monitoring Asphalt Pavements,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Debroux, N.[Noémie], Le Guyader, C.[Carole], Vese, L.A.[Luminita A.],
A Nonlocal Laplacian-Based Model for Bituminous Surfacing Crack Recovery and its MPI Implementation,
JMIV(62), No. 6-7, July 2020, pp. 1007-1033.
Springer DOI 2007
BibRef

Dhiman, A., Klette, R.,
Pothole Detection Using Computer Vision and Learning,
ITS(21), No. 8, August 2020, pp. 3536-3550.
IEEE DOI 2008
Roads, Image reconstruction, Shape, Accelerometers, Cameras, deep learning BibRef


Benz, C., Debus, P., Ha, H.K., Rodehorst, V.,
Crack Segmentation on UAS-based Imagery using Transfer Learning,
IVCNZ19(1-6)
IEEE DOI 2004
Code, Crack Detection.
WWW Link. autonomous aerial vehicles, convolutional neural nets, crack detection, image resolution, image segmentation, UAS BibRef

Park, J.S., Lee, K.S., Kim, S.,
Assessment for a Condition Using Terrestrial Lidar Data,
Gi4DM19(311-314).
DOI Link 1912
Potholes, etc. BibRef

d'Aranno, P., Di Benedetto, A., Fiani, M., Marsella, M.,
Remote Sensing Technologies for Linear Infrastructure Monitoring,
GEORES19(461-468).
DOI Link 1912
E.g. roads. BibRef

Liebold, F., Maas, H.G., Heravi, A.A.,
Crack Width Measurement for Non-planar Surfaces By Triangle Mesh Analysis in Civil Engineering Material Testing,
Optical3D19(107-113).
DOI Link 1912
BibRef

Seydi, S.T., Rastiveis, H.,
A Deep Learning Framework for Roads Network Damage Assessment Using Post-earthquake Lidar Data,
SMPR19(955-961).
DOI Link 1912
BibRef

Fakhri, S.A., Fakhri, S.A., Saadatseresht, M.,
Road Crack Detection Using Gaussian/prewitt Filter,
SMPR19(371-377).
DOI Link 1912
BibRef

Truong-Hong, L., Laefer, D.F., Lindenbergh, R.C.,
Automatic Detection of Road Edges From Aerial Laser Scanning Data,
Laser19(1135-1140).
DOI Link 1912
BibRef

van der Horst, B.B., Lindenbergh, R.C., Puister, S.W.J.,
Mobile Laser Scan Data for Road Surface Damage Detection,
Laser19(1141-1148).
DOI Link 1912
BibRef

König, J., Jenkins, M.D.[M. David], Barrie, P., Mannion, M., Morison, G.,
A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating,
ICIP19(1460-1464)
IEEE DOI 1910
Semantic Segmentation, Attention, Residual Connections, U-Net, Surface Cracks BibRef

Dhiman, A., Chien, H., Klette, R.,
Road surface distress detection in disparity space,
IVCNZ17(1-6)
IEEE DOI 1902
road accidents, road traffic, roads, stereo image processing, road surface distress detection, traffic accidents, Sensors BibRef

Yang, L., Li, B., Li, W., Jiang, B., Xiao, J.,
Semantic Metric 3D Reconstruction for Concrete Inspection,
Odometry18(1624-16248)
IEEE DOI 1812
Inspection, Semantics, Measurement, Visualization, Simultaneous localization and mapping, Image segmentation BibRef

Song, W., Workman, S., Hadzic, A., Zhang, X., Green, E., Chen, M., Souleyrette, R., Jacobs, N.,
FARSA: Fully Automated Roadway Safety Assessment,
WACV18(521-529)
IEEE DOI 1806
image processing, neural nets, road safety, roads, traffic engineering computing, FARSA, US Road Assessment Program, Training BibRef

Liu, X.Z.[Xiang-Zeng], Ai, Y.F.[Yun-Feng], Scherer, S.[Sebastian],
Robust image-based crack detection in concrete structure using multi-scale enhancement and visual features,
ICIP17(2304-2308)
IEEE DOI 1803
Indexes, Crack detection, concrete structure, guided filter, image enhancement BibRef

Güldür Erkal, B., Apaydin, N.M.,
Bridge Surface Damage Detection Application with A Laser-based Software Prototype,
GeoAdvances17(55-57).
DOI Link 1805
BibRef

Grünauer, A.[Andreas], Halmetschlager-Funek, G.[Georg], Prankl, J.[Johann], Vincze, M.[Markus],
Learning the Floor Type for Automated Detection of Dirt Spots for Robotic Floor Cleaning Using Gaussian Mixture Models,
CVS17(576-589).
Springer DOI 1711
BibRef

Chaudhury, S., Nakano, G., Takada, J., Iketani, A.,
Spatial-Temporal Motion Field Analysis for Pixelwise Crack Detection on Concrete Surfaces,
WACV17(336-344)
IEEE DOI 1609
Bridges, Concrete, Labeling, Loading, Maintenance engineering, Safety, Surface cracks. BibRef

Martínez-Sánchez, J., Puente, I., GonzálezJorge, H., Riveiro, B., Arias, P.,
Automatic Thickness And Volume Estimation Of Sprayed Concrete On Anchored Retaining Walls From Terrestrial Lidar Data,
ISPRS16(B5: 521-526).
DOI Link 1610
BibRef

Abdic, I., Fridman, L., Brown, D.E., Angell, W., Reimer, B., Marchi, E., Schuller, B.,
Detecting road surface wetness from audio: A deep learning approach,
ICPR16(3458-3463)
IEEE DOI 1705
Cameras, Data collection, Recurrent neural networks, Roads, Rough surfaces, Spectrogram, Tires BibRef

Vandoni, J., Le Hégarat-Mascle, S., Aldea, E.,
Crack detection based on a Marked Point Process model,
ICPR16(3933-3938)
IEEE DOI 1705
Adaptation models, Data models, Extremities, Image segmentation, Joining processes, Roads, Robustness BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Paint and Printing Quality, Print Analysis .


Last update:Sep 14, 2020 at 15:32:18