Abstract
A machine vision inspection model of surface defects, inspired by the methodologies of neuroanatomy and psychology, is investigated. Firstly, the features extracted from defect images are combined into a saliency map. The bottom-up attention mechanism then obtains ‘‘what’’ and ‘‘where’’ information. Finally, the Markov model is used to classify the types of the defects. Experimental results demonstrate the feasibility and effectiveness of the proposed model with 94.40% probability of accurately detecting of the existence of cropper strips defects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Zheng, H., Kong, L., Nahavandi, S.: Automatic Inspection of Metallic Surface Defects using Genetic Algorithms. Journal of Materials Processing Tech. 125, 427–433 (2002)
Liang, R., Ding, Y., Zhang, X., Chen, J.: Copper Strip Surface Defects Inspection Based on SVM-RBF. In: 4th International Conference on Natural Computation, pp. 41–45. IEEE Press, New York (2008)
Zhong, K.-H., Ding, M.-Y., Zhou, C.-P.: Texture Defect Inspection Method using Difference Statistics Feature in Wavelet Domain. Systems Engineering and Electronics 26, 660–665 (2004)
Zhang, X., Liang, R., Ding, Y., Chen, J., Duan, D., Zong, G.: The System of Copper Strips Surface Defects Inspection Based on Intelligent Fusion. In: 2008 IEEE International Conference on Automation and Logistics, pp. 476–480. IEEE Press, New York (2008)
Li, T.-S.: Applying Wavelets Transform, Rough Set Theory and Support Vector Machine for Copper Clad Laminate Defects Classification. Expert Systems with Applications 36, 5822–5829 (2009)
Luo, S.-W.: Information Processing Theory of Visual Perception. publishing house of electronics industry, Beijing (2006)
Noton, D., Stark, L.: Eye Movements and Visual Perception. Scientific American 224, 35–43 (1971)
Didday, R., Arbib, M.: Eye Movements and Visual Perception: A Two Visual System Model. International Journal of Man-Machine Studies 7, 547–570 (1975)
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Rimey, R., Brown, C.: Selective Attention as Sequential Behavior: Modeling Eye Movements with An Augmented Hidden Markov Model. Department of Computer Science, University of Rochester (1990)
Salah, A., Alpaydin, E., Akarun, L.: A Selective Attention-based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 420–425 (2002)
Corbetta, M.: Frontoparietal Cortical Networks for Directing Attention and The Eye to Visual locations: Identical, independent, or overlapping neural systems? Proc. Natl. Acad. Sci. USA 95, 831–838 (1998)
Vazquez, E., Gevers, T., Lucassen, M., Weijer, J., Baldrich, R.: Saliency of Color Image Derivatives: A Comparison between Computational Models and Human Perception. J. Opt. Soc. Am. A 27, 613–621 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, XF., Xu, LZ., Zhang, XW., Gong, F., Shi, AY., Wang, HB. (2011). A Model of Saliency-Based Selective Attention for Machine Vision Inspection Application. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-20267-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20266-7
Online ISBN: 978-3-642-20267-4
eBook Packages: Computer ScienceComputer Science (R0)