Abstract
This paper presents a new approach to image segmentation of colour images for automatic pipe inspection. Pixel-based segmentation of colour images is carried out by a support vector machine (SVM) labelling pixels on the basis of local features. Segmentation can be effected by this pixel labelling together with connected component labelling. The method has been tested using RGB, HSB, Gabor, local window and HS feature sets and is seen to work best with the HSB feature set.
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References
Kirkham, R., Kearney, P.D., Rogers, K.J., Mashford, J.: PIRAT - A system for quantitative sewer pipe assessment. The International Journal of Robotics Research 19(11), 1033–1053 (2000)
Mashford, J.S.: A neural network image classification system for automatic inspection. In: Proc. Of the 1995 IEEE International Conference on Neural Networks, Perth, Australia (1995)
Blanz, W.E., Gish, S.L.: A real-time image segmentation system using a connectionist classifier architecture. International Journal of Pattern Recognition and Artificial Intelligence 5(4), 603–617 (1991)
Gomez-Moreno, H., Gil-Jimenez, P., Lafuente-Arroyo, S., Vicen-Bueno, R., Sanchez-Montero, R.: Color image segmentation using the support vector machines. Recent Advances in Intelligent Systems and Signal Processing, 151–155 (2003)
Gonzalez, R., Woods, R.: Digital Image Processing, 2nd edn., p. 295. Prentice Hall Press, Englewood Cliffs (2002)
Randen, T., Husøy, J.H.: Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Machine Intell. 21(4), 291–310 (1999)
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© 2007 Springer-Verlag Berlin Heidelberg
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Mashford, J., Davis, P., Rahilly, M. (2007). Pixel-Based Colour Image Segmentation Using Support Vector Machine for Automatic Pipe Inspection. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_85
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DOI: https://doi.org/10.1007/978-3-540-76928-6_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
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