Abstract
Automated visual inspection is defined as a quality control task that determines automatically if a product, or test object, deviates from a given set of specifications using visual data. In the last 25 years, many research directions in this field have been exploited, some very different principles have been adopted and a wide variety of algorithms have been appeared in the literature. However, automated visual inspection systems still suffer from i) detection accuracy, because there is a fundamental trade off between false alarms and miss detections; and ii) strong bottleneck derived from mechanical speed and from high computational cost. For this reasons, automated visual inspection remains an open question. In this sense, Automated Multiple View Inspection, a robust method that uses redundant views of the test object to perform the inspection task, is opening up new possibilities in inspection field by taking into account the useful information about the correspondence between the different views. This strategy is very robust because in first step it identifies potential defects in each view and in second step it finds correspondences between potential defects, and only those that are matched in different views are detected as real defects. In this paper, we review the advances done in this field giving an overview of the multiple view methodology and showing experimental results obtained on real data.
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References
Malamas, E., Petrakis, E., Zervakis, M.: A survey on industrial vision systems, applications and tools. Image and Vision Computing 21, 171–188 (2003)
Newman, T., Jain, A.: A survey of automated visual inspection. Computer Vision and Image Understanding 61, 231–262 (1995)
Chin, R.: Automated visual inspection: 1981-1987. Computer Vision Graphics Image Process 41, 346–381 (1988)
Chin, R.T., Harlow, C.: Automated visual inspection: A survey. IEEE Trans. Pattern Analysis and Machine Intelligence 4, 557–573 (1982)
Mery, D.: Automated radioscopic testing of aluminum casting. Materials Evaluation 64, 135–143 (2006)
Castleman, K.: Digital image processing. Prentice-Hall, Englewood Cliffs (1996)
Purschke, M.: IQI-sensitivity and applications of flat panel detectors and X-ray image intensifiers - a comparison. Insight 44, 628–630 (2002)
Davis, E.: Machine Vision, 3rd edn. Morgan Kaufmann Publishers, San Francisco (2005)
Trussell, H., Saber, E., Vrhel, M.: Color image processing. IEEE Signal Processing Magazine 22, 14–22 (2005)
Nagy, J., Palmer, K., Perrone, L.: Iterative methods for image deblurring: A Matlab object-oriented approach. Numerical Algorithms 36, 73–93 (2005)
Banham, M., Katsaggelos, A.: Digital image restoration. IEEE Signal Processing Magazine 14, 24–40 (1997)
Zhang, Y.J.: An Overview of Image and Video Segmentation in the Last 40 Years. In: Zhang, Y.-J. (ed.) Advances in Image and Video Segmentation, pp. 1–15. IRM Press, Idea Group Inc, Hershey (2006)
Cheng, H., Jiang, X., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)
Koschan, A., Abidi, M.: Detection and classification of edges in color images: A review of vector-valued techniques. IEEE Signal Processing Magazine 22, 64–73 (2005)
Mery, D.: Crossing line profile: a new approach to detecting defects in aluminium castings. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003, vol. 2749, pp. 725–732. Springer, Heidelberg (2003)
Mery, D., Berti, M.: Automatic detection of welding defects using texture features. Insight 45, 676–681 (2003)
Webb, A.: Statistical Pattern Recognition, 2nd edn. John Wiley and Sons Ltd, New Jersey (2005)
Jain, A., Duin, R., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Somol, P., Pudil, P., Kittler, J.: Fast branch and bound algorithms for optimal feature selection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 900–912 (2004)
Mery, D., da Silva, R., Caloba, L., Rebello, J.: Pattern recognition in the automatic inspection of aluminium castings. Insight 45, 475–483 (2003)
Carvajal, K., Chacón, M., Mery, D., Acuña, G.: Neural network method for failure detection with skewed class distribution. Insight 46, 399–402 (2004)
Kay, S.: Fundamentals of Statistical Signal Processing: Detection Theory. Processing Series, vol. 2. Prentice Hall Signal, New Jersey (1998)
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning Journal 42, 203–231 (2001)
Manolakis, D., Shaw, G.: Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine 19, 29–43 (2002)
Mery, D., Hahn, D., Hitschfeld, N.: Simulation of defects in aluminium castings using cad models of flaws and real X-ray images. Insight 47, 618–624 (2005)
Raina, R., Shen, Y., Ng, A.Y., McCallum, A.: Classification with hybrid generative/discriminative models. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in neural information processing systems, vol. 16. MIT Press, Cambridge (2003)
Theodoris, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Academic Press, Elsevier (2003)
Bishop, C.: Neural Network for Pattern Recognition. Oxford University Press Inc., New York (1997)
Shawe-Taylo, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2005)
Kuncheva, L.: A theoretical study on six classifier fusion strategies. IEEE Trans. on Pattern Analysis and Machine Learning 24, 281–286 (2002)
Mery, D., Chacón, M., Muñoz, L., Gonzalez, L.: Automated inspection of aluminium castings using fusion strategies. Materials Evaluation 63, 148–153 (2005)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)
Mery, D.: Exploiting multiple view geometry in X-ray testing: Part I: Theory. Materials Evaluation 61, 1226–1233 (2003)
Mery, D.: Exploiting multiple view geometry in X-ray testing: Part II: Applications. Materials Evaluation 61, 1311–1314 (2003)
Mitchell, T.: Machine Learning. McGraw-Hill, Boston (1997)
Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robotics and Automation 18, 890–901 (2002)
Mery, D.: Explicit geometric model of a radioscopic imaging system. NDT & E International 36, 587–599 (2003)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)
Mery, D., Carrasco, M.: Automated multiple view inspection based on uncalibrated image sequences. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005, vol. 3540, pp. 1238–1247. Springer, Heidelberg (2005)
Carrasco, M., Mery, D.: Automated visual inspection using trifocal analysis in an uncalibrated sequence of images. Materials Evaluation 64 (2006) (in Press)
Mery, D., Ochoa, F., Vidal, R.: Tracking of points in a calibrated and noisy image sequence. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004, vol. 3211, pp. 647–654. Springer, Heidelberg (2004)
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Mery, D., Carrasco, M. (2006). Advances on Automated Multiple View Inspection. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_51
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DOI: https://doi.org/10.1007/11949534_51
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