Abstract
Automatic visual inspection has become an important application of pattern recognition, as it supports the human in this demanding and often dangerous work. Nevertheless, often missing abnormal or defective samples prohibit a supervised learning of defect models. For this reason, techniques known as one-class classification and novelty- or unusual event detection have arisen in the past years. This paper presents a new strategy to employ Hidden Markov models for defect localization in wire ropes. It is shown, that the Viterbi scores can be used as indicator for unusual subsequences. This prevents a partition of the signal into sufficient small signal windows at cost of the temporal context. Our results outperform recent time-invariant one-class classification approaches and depict a great advance for an automatic visual inspection of wire ropes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mäenpää, T., Turtinen, M., Pietikäinen, M.: Real-time surface inspection by texture. Real-Time Imaging 9(5), 289–296 (2003)
Pernkopf, F.: 3D Surface Analysis using Coupled HMMs. Machine Vision and Applications 16(5), 298–305 (2005)
Xie, X., Mirmehdi, M.: TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(8), 1454–1464 (2007)
Xie, X.: A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques. Electronic Letters on Computer Vision and Image Analysis 7(3), 1–22 (2008)
Moll, D.: Innovative procedure for visual rope inspection. Lift Report 29(3), 10–14 (2003)
EN 12927-7: Safety requirments for cableways installations designed to carry persons. ropes. Inspection, repair and maintenance. European Norm: EN 12927-7:2004 (2004)
Tax, D.M.J.: One-class classification - Concept-learning in the absence of counter-examples. Phd thesis, Technische Universitt Delft (2001)
Platzer, E.S., Denzler, J., Süße, H., Nägele, J., Wehking, K.H.: Robustness of Different Features for One-class Classification and Anomaly Detection in Wire Ropes. In: Proceedings of the 4th International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 171–178 (2009)
Markou, M., Singh, S.: Novelty detection: a review - part 1: statistical approaches. Signal Processing 83(12), 2481–2497 (2003)
Hodge, V., Austin, J.: A Survey of Outlier Detection Methodologies. Artificial Intelligence Review 22(2), 85–126 (2004)
Xie, X., Mirmehdi, M.: Localising Surface Defects in Random Colour Textures using Multiscale Texem Analysis in Image Eigenchannels, pp. 1124–1127 (2005)
Platzer, E.S., Denzler, J., Süße, H., Nägele, J., Wehking, K.H.: Challenging Anomaly Detection in Wire Ropes Using Linear Prediction Combined with One-class Classification. In: Proceedings of the 13th International Fall Workshop Vision, Modeling and Visualization, pp. 343–352 (2008)
Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-supervised adapted HMMs for unusual event detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 611–618 (2005)
Brewer, N., Nianjun, L., Vel, O.D., Caelli, T.: Using Coupled Hidden Markov Models to Model Suspect Interactions in Digital Forensic Analysis. In: International Workshop on Integrating AI and Data Mining, pp. 58–64 (2006)
Hadizadeh, H., Shokouhi, B.: Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns. IEICE Transactions on Information and Systems E91-D(7), 1937–1945 (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Collobert, R., Bengio, S., Mariéthoz, J.: Torch: a modular machine learning software library. Technical report, IDIAP (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Platzer, ES., Nägele, J., Wehking, KH., Denzler, J. (2009). HMM-Based Defect Localization in Wire Ropes – A New Approach to Unusual Subsequence Recognition. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-03798-6_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03797-9
Online ISBN: 978-3-642-03798-6
eBook Packages: Computer ScienceComputer Science (R0)