Development Of Pipeline Corrosion Inspection System Using Machine Vision

These days, utilization of camera as an inspection tool has been expanded. The flexible function of camera is adequate to obtain different kind of information. In Cawley (2001) review on NDT that was presented in 2001, Radiography, Ultrasonic, Eddy Current, Magnetic Particle, and Penetrant Testing w...

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Main Author:	Idris, Syahril Anuar
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/20463/1/Development%20Of%20Pipeline%20Corrosion%20Inspection%20System%20Using%20Machine%20Vision.pdf http://eprints.utem.edu.my/id/eprint/20463/2/Development%20Of%20Pipeline%20Corrosion%20Inspection%20System%20Using%20Machine%20Vision.pdf
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id	my-utem-ep.20463
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Idris, Syahril Anuar Development Of Pipeline Corrosion Inspection System Using Machine Vision
description	These days, utilization of camera as an inspection tool has been expanded. The flexible function of camera is adequate to obtain different kind of information. In Cawley (2001) review on NDT that was presented in 2001, Radiography, Ultrasonic, Eddy Current, Magnetic Particle, and Penetrant Testing were the top five techniques dominating the NDT market yet Visual Inspection is the most widely applied. Even though the popularity of visual inspection is higher compared to other NDT method, but due to the reliability issues it is often used together with other methods. This research work is focusing on developing a robust corrosion inspection system based on vision sensor that is able to accurately detect and classify corrosion based on the appearance features. By installing at an early stage, inspection system would be able to gather data and at the same time identify and analyse the collected data. Through the results, the analysed data is able to classify the corrosion type based on appearance. From the research work, the method of using image enhancement filters to improve accuracy of vision corrosion inspection system is identified. The detection of each macroscopic surface corrosion types; galvanic; crevice; erosion; pitting and exfoliation using vision inspection able to achieve 79% accuracy using the simulated dataset. The new method of corrosion inspection operation which able to generate prevention plan has qualified the Vision Corrosion Inspection System to be used during preliminary inspection. It is expected that the Vision Corrosion Inspection System can improve vision inspection as the pioneer in NDT method for corrosion inspection. In addition, framework of the developed Vision Corrosion Inspection system is applicable for other applications of vision inspection whereby it can be applied for other inspection process or extending its application to other problems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Idris, Syahril Anuar
author_facet	Idris, Syahril Anuar
author_sort	Idris, Syahril Anuar
title	Development Of Pipeline Corrosion Inspection System Using Machine Vision
title_short	Development Of Pipeline Corrosion Inspection System Using Machine Vision
title_full	Development Of Pipeline Corrosion Inspection System Using Machine Vision
title_fullStr	Development Of Pipeline Corrosion Inspection System Using Machine Vision
title_full_unstemmed	Development Of Pipeline Corrosion Inspection System Using Machine Vision
title_sort	development of pipeline corrosion inspection system using machine vision
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Manufacturing Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/20463/1/Development%20Of%20Pipeline%20Corrosion%20Inspection%20System%20Using%20Machine%20Vision.pdf http://eprints.utem.edu.my/id/eprint/20463/2/Development%20Of%20Pipeline%20Corrosion%20Inspection%20System%20Using%20Machine%20Vision.pdf
_version_	1747833967015362560
spelling	my-utem-ep.204632021-10-10T22:47:10Z Development Of Pipeline Corrosion Inspection System Using Machine Vision 2016 Idris, Syahril Anuar T Technology (General) TA Engineering (General). Civil engineering (General) These days, utilization of camera as an inspection tool has been expanded. The flexible function of camera is adequate to obtain different kind of information. In Cawley (2001) review on NDT that was presented in 2001, Radiography, Ultrasonic, Eddy Current, Magnetic Particle, and Penetrant Testing were the top five techniques dominating the NDT market yet Visual Inspection is the most widely applied. Even though the popularity of visual inspection is higher compared to other NDT method, but due to the reliability issues it is often used together with other methods. This research work is focusing on developing a robust corrosion inspection system based on vision sensor that is able to accurately detect and classify corrosion based on the appearance features. By installing at an early stage, inspection system would be able to gather data and at the same time identify and analyse the collected data. Through the results, the analysed data is able to classify the corrosion type based on appearance. From the research work, the method of using image enhancement filters to improve accuracy of vision corrosion inspection system is identified. The detection of each macroscopic surface corrosion types; galvanic; crevice; erosion; pitting and exfoliation using vision inspection able to achieve 79% accuracy using the simulated dataset. The new method of corrosion inspection operation which able to generate prevention plan has qualified the Vision Corrosion Inspection System to be used during preliminary inspection. It is expected that the Vision Corrosion Inspection System can improve vision inspection as the pioneer in NDT method for corrosion inspection. In addition, framework of the developed Vision Corrosion Inspection system is applicable for other applications of vision inspection whereby it can be applied for other inspection process or extending its application to other problems. 2016 Thesis http://eprints.utem.edu.my/id/eprint/20463/ http://eprints.utem.edu.my/id/eprint/20463/1/Development%20Of%20Pipeline%20Corrosion%20Inspection%20System%20Using%20Machine%20Vision.pdf text en public http://eprints.utem.edu.my/id/eprint/20463/2/Development%20Of%20Pipeline%20Corrosion%20Inspection%20System%20Using%20Machine%20Vision.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=105854 phd doctoral Universiti Teknikal Malaysia Melaka Faculty Of Manufacturing Engineering 1. Abellan-Nebot, J.V., 2010. A Review of Artificial Intelligent Approaches Applied to Part Accuracy Prediction. In International Journal of Machining and Machinability of Materials, Vol.8, No. 1-2, pp.6-37. 2. Abramovich, G., Weng, J. and Dutta, D., 2005. Adaptive Part Inspection Through Developmental Vision. In Journal of Manufacturing Science and Engineering, Vol.127, No. 4, pp.846-856. 3. Addiati, D., Sheikh, U.U. and Bakar, S.A., 2009. Restoration of Out of Focus Barcode Images using Wiener Filter. In IEEE 2009 Fifth International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 133-137. 4. Adhikari, R.S., Moselhi, O. and Bagchi, A., 2013. A Study of Image-based Element Condition Index for Bridge Inspection. In IEEE ISRAC 2013 - 30th International Symposium on Automated and Robotics in Construction and Mining. pp. 345-356. 5. Allgaier, M. W. and Ness, D., 1993. ASNT Nondestructive Testing Handbook: Visual and Optical testing, Vol. 8, United State of America: American Society of Non-destructive Testing. 6. American Society for Non-destructive Testing, 2016. In ASNT Certification. [online] Available at: https://www.asnt.org/MajorSiteSections/Certification [Accessed on 11 April 2016] 7. Andreopoulos, A. and Tsotsos, J.K., 2013. 50 Years of Object Recognition: Directions forward. In Computer Vision and Image Understanding, Vol.117, No.8, pp.827-891. 8. Anonymous, 2005. 2004-2005 ASNT President Henry Stephens, In Presidential Profile, M.E., Vol. 63, No. 1, pp. 20-22. 9. Ashfal, C.R., 1992. Robot and Manufacturing Automation. 2nd Edition. United State of America: John Wiley & Sons, Inc. 10. ASM International, 2008. Comparison of NDT Methods Magnetic Particle Inspection Scanning Auger Microprobe. NDT Guide. to Non-Destructive Test., vol. 166, No. 6, pp. 10. 11. Assaleh, K., Shanableh, T. and Yehia, S., 2015. Artificial Intelligence and Signal Processing for Infrastructure Assessment. In Proc. SPIE 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, pp. 94372O. 12. Bachnak, R., Funtanilla, J. and Hernandez, J., 2004. LEDs as Light Source: Examining Quality of Acquired Images. In Electronic Imaging 2004, pp. 99-107. 13. Barry, R. and Heiser, G., 1978. Evaluation of The Quality of Electron Beam Butt Welds in High Strength Maraging Steel Rings. Virginia: Defense Technical Information Center. 14. Berens, A.P., 2001. What Metric Should Be Used to Measure Intrinsic Capability? Is it Only Probability of Detection and False Call Probability, or is Theoretical? In Materials Evaluation, Vol.59, No.1, pp.42-48. 15. Bonnín-Pascual, F. and Ortiz, A., 2010. Detection of Cracks and Corrosion for Automated Vessels Visual Inspection. In Proc of 13th International Congress of the Catalan Association of Artificial Intelligence, CCIA 2010. pp. 111-120. 16. Bouillant, S., Miteran, J., Paindavoine, M. and Meriaudeau, F., 2004. Real-time Flaw Detection on Complex Part: Classification with SVM and Hyper Rectangle-Based Method. In Electronic Imaging 2004. pp. 170-177. 17. Boussion, N., Hatt, M., Lamare, F., Bizais, Y., Turzo, A., Cheze-Le Rest, C. and Visvikis, D., 2006. A Multiresolution Image based Approach for Correction of Partial Volume Effects in Emission Tomography. Physics in Medicine and Biology, Vol.51, No.7, pp.1857. 18. Bouzerdoum, A., Havstad, A. and Beghdadi, A., 2004. Image Quality Assessment Using a Neural Network Approach. In Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004. pp. 330-333. 19. Brooks, A.C., Zhao, X. and Pappas, T.N., 2008. Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic Distortions. In IEEE Transactions on Image Processing, Vol.17, No.8, pp.1261-1273. 20. Cabrera, J., Csizmadia, S., Erikson, A., Rauer, H. and Kirste, S., 2012. A Study of the Performance of Transit Detection Tool DST in Space-Based Surveys-Application of The CoRoT Pipeline to Kepler Data. In Astronomy & Astrophysics, Vol.548, pp. A44. 21. Cacciola, M., Megali, G., Pellicanó, D. and Morabito, F.C., 2011. A GMR–ECT Based Embedded Solution for Applications on PCB Inspections. In Sensors and Actuators, A: Physical, Vol.167, No.1, pp.25-33. 22. Carvalho, A.A., Rebello, J.M.A., Souza, M.P.V., Sagrilo, L.V.S. and Soares, S.D., 2008. Reliability of Non-destructive Test Techniques in The Inspection of Pipelines used in The 23. Oil Industry. In International Journal of Pressure Vessels and Piping, Vol.85, No.11, pp.745-751. 24. Cawley, P., 2001. Non-destructive Testing - Current Capabilities and Future Directions. In Journal of Materials Design and Applications, Vol.215, No.4, pp.213-223. 25. Chaturvedi, T.P., 2013. Corrosion Behaviour of Orthodontic Alloys - A Review. Doctoral dissertation, Banaras Hindu University Varanasi. 26. Chen, J., Wang, Y., Wu, C., Zhang, H., Ge, J., Zhou, B. and Mao, J., 2011. Research on Real-time Vibration Insensitive Inspection and Classification Algorithms for Automatic Online Vision-Based Inspector. In 2011 IEEE International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE. pp. 1737-1742. 27. Cherkassky, V. and Mulier, F.M., 2007. Learning from Data: Concepts, Theory, and Methods. John Wiley & Sons. 28. Ching YS, 2015. Keynote Lecture: Methods of Achieving Perfect Recognition Scores. International Asean Conference on Pattern Recognition (ACPR'15) 29. Coelho, P.A., Torres, S.N., Ramírez, W.E., Gutiérrez, P.A., Toro, C.A., Soto, J.G., Sbarbaro, D.G. and Pezoa, J.E., 2016. A machine vision system for automatic detection of parasites Edotea magellanica in shell-off cooked clam Mulinia edulis. Journal of Food Engineering, 181, pp.84-91. 30. Davies, E.R., 2012. Computer and Machine Vision: Theory, Algorithms, Practicalities. Cambridge: Academic Press. 31. Davis, J.R. 2000. Corrosion: Understanding the Basics. Ohio: ASM International. 32. Drury, C.G., 1992. Inspection Performance, Handbook of Industrial Engineering. 2nd Edition. New York: John Wiley & Sons, Inc. 33. Farhidzadeh, A., Ebrahimkhanlou, A. and Salamone, S., 2014. A Vision-Based Technique for Damage Assessment of reinforced concrete structures. In SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring, pp.90642H. 34. Garnier, C., Pastor, M.L., Eyma, F. and Lorrain, B., 2011. The Detection of Aeronautical Defects In-situ on Composite Structures using Non Destructive Testing. In Composite structures, Vol. 93, No.5, pp.1328-1336. 35. Gonzales, R.C., Woods, R.E., 1992. Digital Image Processing, 2nd Edition, United State of America: Addison - Wesley Publication. 36. Gordillo, N., Montseny, E. and Sobrevilla, P., 2013. State of The Art Survey on MRI Brain Tumor Segmentation. In Magnetic Resonance Imaging, Vol.31, No.8, pp.1426-1438. 37. Groover, M.P., 2001. Automation, Production System, and Computer - Intergrated Manufacturing. 2nd Edition. New Jersey: Prentice Hall. 38. Groysman, A., 2009. Corrosion Monitoring. Corrosion Reviews, Vol.27, No.4 - 5, pp. 205 - 343. 39. Haniff, H.M., Sulaiman, M., Shah, H.N.M. and Teck, L.W., 2011. Shape-based matching: Defect inspection of glue process in vision system. In 2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA, pp. 53-57. 40. Hata, S., Ishimaru, I., Hirokari, M., Yabuutchi, H. and Masuda, S.I., 2001. Color Pattern Inspection Machine with Human Sensitivity. In Proceedings. 10th IEEE International Workshop on Robot and Human Interactive Communication, 2001. pp. 68-73. 41. Howard, I.P., 2012. Perceiving in Depth, Volume 1: Basic Mechanisms. Oxford: Oxford University Press. 42. Ishii, I., 2015. Fast Three-Dimensional Shape Inspection Using a Multi-sided Mirror. In Integrated Imaging and Vision Techniques for Industrial Inspection. pp. 177-199. 43. Jahanshahi, M.R. and Masri, S.F., 2012. Parametric Performance Evaluation of Wavelet-Based Corrosion Detection Algorithms for Condition Assessment of Civil Infrastructure Systems. In Journal of Computing in Civil Engineering, Vol.27, No.4, pp.345-357. 44. Jahanshahi, M.R., Kelly, J.S., Masri, S.F. and Sukhatme, G.S., 2009. A Survey and Evaluation of Promising Approaches for Automatic Image-Based Defect Detection of Bridge Structures. In Structure and Infrastructure Engineering, Vol.5, No.6, pp.455-486. 45. Jeong, B.G., Kim, H.S., Kim, S.C. and Eom, I.L., 2008. Edge-Adaptive Demosaicking for Reducing Artifact along Line Edge. In IEEE Congress on Image and Signal Processing, 2008, CISP'08. Vol. 3, pp. 316-319. 46. Johnson, S.A., 2015. Nanoscopy for Nanoscience: How Super-Resolution Microscopy Extends Imaging for Nanotechnology. In Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, Vol.7, No.3, pp.266-281. 47. Jonic, S., Sorzano, C.O.S. and Boisset, N., 2008. Comparison of Single-Particle Analysis and Electron Tomography Approaches: An Overview. In Journal of microscopy, Vol.232, No.3, pp.562-579. 48. Kantola, K. and Tenno, R., 2009. Machine Vision in Detection of Corrosion Products on SO 2 Exposed ENIG Surface and an In-situ Analysis of the Corrosion Factors. In Journal of Materials Processing Technology, Vol.209, No.5, pp.2707-2714. 49. Khalid, N.E.A., Manaf, M., Aziz, M.E., Noor, N.M. and Zainal, N., 2008. Rule Based Fuzzy (RBF) Membership Edge Detection on Hand Phantom Radiograph Images. In IEEE International Symposium on Information Technology 2008, ITSim 2008. Vol. 1, pp. 1-6. 50. Köhler, T., Hunter, L.J., 2014. Process Compensated Resonance Testing: Whole Body Surface NDT for Ceramic Balls. In InterCeram: International Ceramic Review. Vol.63, No.4, pp. 178-181. 51. Kolkoori, S., Wrobel, N., Hohendorf, S., Redmer, B. and Ewert, U., 2015. Mobile High-Energy X-ray Radiography for Nondestructive Testing of Cargo Containers. In Materials Evaluation, Vol.73, No.2. 52. Kwak, I., Morris, A., Silver, D., Kratzer, E., Vandapel, N. and Thayer, S., 2007. LADAR-Based Pipeline Inspection and Location. In Proceedings of the ASCE International Conference on Pipeline Engineering and Construction: Advances and Experiences with Trenchless Pipeline Projects 2007. pp. 83. 53. Liang, Z., Hong-yi, L. and Pei-xin, Y., 2010. Study on Image Identification Method of In-service Pipeline Corrosion Fault. In IEEE Second International Conference on Information Technology and Computer Science, ITCS 2010. pp. 182-185. 54. Lin, L., Yang, P., Zhang, D., Zhao, T., Yang, H. and Li, X., 2012. Review of Phased Array Ultrasonic Testing for Thick Wall cast Austenitic Stainless Steel Pipeline Welds. In Jixie Gongcheng Xuebao(Chinese Journal of Mechanical Engineering), Vol.48, No.4, pp.12-20. 55. Livens, S., Scheunders, P., Van de Wouwer, G., Van Dyck, D., Smets, H., Winkelmans, J. and Bogaerts, W., 1996. A Texture Analysis Approach to Corrosion Image Classification. In Microscopy Microanalysis Microstructures, Vol.7, No.2, pp.143-152. 56. Maalej, M., Ahmed, S.F.U., Kuang, K.S.C., Paramasivam, P., 2004. Fiber Optic Sensing for Monitoring Corrosion-Induced Damage, In Structural Health Monitoring, Vol.3, No.2, pp. 165 - 176. 57. Mariño, P., Pastoriza, V., Santamaría, M. and Martínez, E., 2005. Image Processing Application with A TSK Fuzzy Model. In Fuzzy Systems and Knowledge Discovery. pp. 950-960. 58. Marinov, O., Deen, M.J. and Iniguez, B., 2005. Charge Transport in Organic and Polymer Thin-film Transistors: Recent Issues. In IEEE Proceedings on Circuits, Devices and Systems. Vol. 152, No. 3, pp. 189-209. 59. Medeiros, F.N., Ramalho, G.L., Bento, M.P. and Medeiros, L.C., 2010. On The Evaluation of Texture and Color Features for Nondestructive Corrosion Detection. In EURASIP Journal on Advances in Signal Processing. pp.7. 60. Medina, R., Gayubo, F., González-Rodrigo, L.M., Olmedo, D., Gómez-García-Bermejo, J., Zalama, E. and Perán, J.R., 2011. Automated Visual Classification of Frequent Defects in Flat Steel Coils. In The International Journal of Advanced Manufacturing Technology, Vol.57, No.9-12, pp.1087-1097. 61. Merazi Meksen, T., Boudraa, M., Boudraa, B., 2011. Automatic Selection of Relevant Data During Ultrasonic Inspection. In 23rd European Modelling and Simulation Symposium, EMSS 2011. pp. 460-464. 62. Moeslund, T.B., Hilton, A. and Krüger, V., 2006. A Survey of Advances in Vision-based Human Motion Capture and Analysis. In Computer vision and image understanding, Vol.104, No.2, pp.90-126. 63. Moon, S. and Bernold, L.E., 1997. Vision-based Interactive Path Planning for Robotic Bridge Paint Removal. In Journal of Computing in Civil Engineering, Vol.11, No.2, pp.113-120. 64. Morala-Argüello, P., Barreiro, J. and Alegre, E., 2012. An Evaluation of Surface Roughness Classes by Computer Vision Using Wavelet Transform in The Frequency Domain. In The International Journal of Advanced Manufacturing Technology, Vol.59, No.1-4, pp.213-220. 65. Motamedi, M., Faramarzi, F. and Duran, O., 2012. New Concept for Corrosion Inspection of Urban Pipeline Networks by Digital Image Processing. In 38th Annual Conference on IEEE Industrial Electronics Society, IECON 2012. pp. 1551-1556. 66. Murray, P., West, G., Marshall, S. and McArthur, S., 2016. Automated In-core Image Generation from Video to Aid Visual Inspection of Nuclear Power Plant Cores. In Nuclear Engineering and Design, Vol. 300, pp.57-66. 67. Nagarajan, R., Yaacob, S., Pandian, P., Karthigayan, M., Amin, S.H. and Khalid, M., 2007. A Real Time Marking Inspection Scheme for Semiconductor Industries. In The International Journal of Advanced Manufacturing Technology, Vol.34, No.9-10, pp.926-932. 68. Neupane, B., Ligler, F.S. and Wang, G., 2014. Review of Recent Developments in Stimulated Emission Depletion Microscopy: Applications on Cell Imaging. In Journal of Biomedical Optics, Vol.19, No.8, pp.080901-080901. 69. Ortiz, A., Simó, M. and Oliver, G., 2000. Image Sequence Analysis for Real-time Underwater Cable Tracking. In Fifth IEEE Workshop on. Applications of Computer Vision, pp. 230-236. 70. Padmavathi, G., Subashini, P., Kumar, M.M. and Thakur, S.K., 2010. Comparison of Filters Used for Underwater Image Pre-processing. In International Journal of Computer Science and Network Security, IJCSNS. Vol.10, No.1, pp.58-65. 71. Paragios, N., Chen, Y. and Faugeras, O.D., 2006. Handbook of Mathematical Models in Computer Vision. Berlin: Springer Science & Business Media. 72. Perng, D.B., Chen, S.H. and Chang, Y.S., 2010. A Novel Internal Thread Defect Auto-Inspection System. In The International Journal of Advanced Manufacturing Technology, Vol.47, No.5-8, pp.731-743. 73. Pidaparti, R.M., Hinderliter, B. and Maskey, D., 2013. Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis. In International Scholarly Research Notices: Corrosion, 2013. 74. Punj, D., Ghenuche, P., Moparthi, S.B., de Torres, J., Grigoriev, V., Rigneault, H. and Wenger, J., 2014. Plasmonic Antennas and Zero-mode Waveguides to Enhance Single Molecule Fluorescence Detection and Fluorescence Correlation Spectroscopy Toward Physiological Concentrations. In Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, Vol.6, No.3, pp.268-282. 75. Pure Technologies, 2013. Case Study: City of Calgary - Memorial Feedermain. [online] Available at: https://www.puretechltd.com/media/case-studies/thecityofcalgarycompletes successfulconditionassessmentandverificationonc303pipeline [Accessed on 11 April 2016]. 76. Pylyshyn, Z., 1999. Is Vision Continuous with Cognition? The Case for Cognitive Impenetrability of Visual Perception. In Behavioral and Brain Sciences, Vol.22, No.03, pp.341-365. 77. Qian, X., Guo, L. and Yu, B., 2010. An Adaptive Image Filter Based on Decimal Object Scale for Noise Reduction and Edge Detection. In 2010 IEEE International Conference on Intelligent Computation Technology and Automation. pp. 461-465. 78. Rajamani, A. and Krishnaveni, V., 2014. Flaw Detection and Denoising of Images. In Academic Journal of Manufacturing Engineering, Vol.12, No.3. 79. Ramanan, A. and Niranjan, M., 2012. A Review of Codebook Models in Patch-based Visual Object Recognition. In Journal of Signal Processing Systems, Vol.68, No.3, pp.333-352. 80. Ricci, M., Ficola, A., Fravolini, M.L., Battaglini, L., Palazzi, A., Burrascano, P., Valigi, P., Appolloni, L., Cervo, S. and Rocchi, C., 2012. Magnetic Imaging and Machine Vision NDT for The On-line Inspection of Stainless Steel Strips. In Measurement Science and Technology, Vol.24, No.2, pp.025401. 81. Rodríguez, P., 2013. Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review. In Journal of Electrical and Computer Engineering, pp.10. 82. Ros, F., Borla, J. and Harba, R., 2008. Automatic and Real-time Inspection System for Smart Card Module Inspection. In Journal of Electronic Imaging, Vol.17, No.1, pp.013018. 83. Ruz, G.A., Estévez, P.A. and Ramírez, P.A., 2009. Automated Visual Inspection System for Wood Defect Classification using Computational Intelligence Techniques. In International Journal of Systems Science, Vol.40, No.2, pp.163-172. 84. Sakuldee, R., Yamsang, N. and Udomhunsakul, S., 2008. Image Quality Assessment for JPEG and JPEG2000. In IEEE Third International Conference on Convergence and Hybrid Information Technology, 2008. ICCIT'08. Vol. 1, pp. 320-325. 85. Sandhu, J.S., Wang, H., Sonpatki, M.M. and Popek, W.J., 2003. Real-time Full-Field Ultrasonic Inspection of Composites Using Acoustography. In NDE for Health Monitoring and Diagnostics. pp. 99-104. 86. Schneider, C.R. and Rudlin, J.R., 2004. Review of Statistical Methods Used in Quantifying NDT Reliability. In Insight-Non-Destructive Testing and Condition Monitoring, Vol.46, No.2, pp.77-79. 87. See, J.E., 2012. Visual inspection: a review of the literature (No. SAND2012-8590). Sandia National Laboratories. 88. Sehgal, A., Kadarusman, J. and Fife, L.D., 2004. TOUCH: A Robotic Vision System for Underwater Object Tracking. In IEEE Conference on Robotics, Automation and Mechatronics. Vol. 1, pp. 455-460. 89. Shannon, D.R., 2013. The Art of Pigging, Girard Industries. 90. Song, A., Ciesielski, V. and Rogers, P., 2001. Vision System Development by Machine Learning: Mashing Assessment in Brewing. In Applied Artificial Intelligence, Vol.15, No.8, pp.777-795. 91. Tobin Jr, K.W., Gleason, S.S. and Karnowski, T.P., 1998. Adaptation of The Fuzzy K-nearest Neighbour Classifier for Manufacturing Automation. In Photonics West'98 Electronic Imaging, pp. 122-130. 92. Tucson Water, 1999. Case Study: Tucson Water Intervenes Before Critical Pipeline Fails. [online] Available at: http://www.structuraltechnologies.com/sites/default/files/ technical_paper_attachments/Tucson-Water-Strengthening-Approach-to-Address-Thrust-Restraint-for-PCCP_0 [Accessed on 11 April 2016]. 93. Unal, G., Yezzi, A. and Krim, H., 2005. Information-Theoretic Active Polygons for Unsupervised Texture Segmentation. In International Journal of Computer Vision, Vol.62, No.3, pp.199-220. 94. Veredas, F.J., Luque-Baena, R.M., Martín-Santos, F.J., Morilla-Herrera, J.C. and Morente, L., 2015. Wound Image Evaluation with Machine Learning. In Neurocomputing, Vol.164, pp.112-122. 95. Voronoc, A., and Stream, N., 2013. Pigs in The Pipes. [online] Available at: http://www.theengineer.co.uk/issues/november-2013-online/pigs-in-the-pipes/ [Accessed on 11 April 2016]. 96. Wade, N.J., 2007. Image, Eye, and Retina (invited review). JOSA A, Vol.24, No.5, pp.1229-1249. 97. Watson, A.B., Hu, J. and McGowan, J.F., 2001. Digital Video Quality Metric Based on Human Vision. In Journal of Electronic Imaging, Vol.10, No.1, pp.20-29. 98. Wu, D., Zhu, Q.-S., 2015. The Latest Research Progress of Image Dehazing. In Zidonghua Xuebao/Acta Automatica Sinica. Vol.41, No.2, pp. 221-239. 99. Wu, H., Zhang, X., Kuang, Y. and Lu, S., 2008. A Real-time Machine Vision System for Solder Paste Inspection. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2008. AIM 2008. pp. 205-210. 100. Wu, Q., Wang, Y.P., Liu, Z., Chen, T. and Castleman, K.R., 2002. The Effect of Image Enhancement on Biomedical Pattern Recognition. In IEEE Proceedings of the Second Joint on Engineering in Medicine and Biology, 2002. Vol. 2, pp. 1067-1069. 101. Yella, S., Dougherty, M.S. and Gupta, N.K., 2006. Artificial Intelligence Techniques for The Automatic Interpretation of Data from Non-destructive Testing. In Insight-Non-Destructive Testing and Condition Monitoring, Vol.48, No.1, pp.10-20. 102. Yilai, M., Li, L., Po, G. and Xuan, G., 2012. Improving SNR of MFL Detecting Defect Signal of Drill Pipe by Using Pole Piece. In Insight-Non-Destructive Testing and Condition Monitoring, Vol.54, No.9, pp.489-492. 103. Yuan, J., Liu, Y.X. and Shao, S.H., 2013. Coal Transport Tape Control System Based on Machine Vision. In Applied Mechanics and Materials. Vol. 419, pp. 745-750. 104. Zhang, B., 2010. Keynote Lecture: Computer Vision versus Human Vision. In Proceedings 9th IEEE International Conference on Cognitive Informatics (ICCI'10). 105. Zhao, J., Yang, Y.O. and Li, G., 2010. The Cold Rolling Strip Surface Defect On-line Inspection System Based on Machine Vision. In IEEE Second Pacific-Asia Conference on Circuits, Communications and System (PACCS), 2010. Vol. 1, pp. 402-405. 106. Zheng, J., Xie, W.F., Viens, M., Birglen, L. and Mantegh, I., 2015. Design of an Advanced Automatic Inspection System for Aircraft Parts Based on Fluorescent Penetrant Inspection Analysis. In Insight-Non-Destructive Testing and Condition Monitoring, Vol.57, No.1, pp.18-34. 107. Zhu, Y.K., Tian, G.Y., Lu, R.S. and Zhang, H., 2011. A Review of Optical NDT Technologies. In Sensors, Vol.11, No.8, pp.7773-7798.

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