Abstract
In general, we can easily determine the manufacturing step that does not function properly by referring to the flaw type. However, a successful segmentation of flaws is the prerequisite for the success of the subsequent flaw classification. It is worth noticing that, different segmentation methods are needed for different types of images. In the study, a mechanism that is capable of choosing a proper segmentation method automatically has been proposed. The mechanism employed artificial neural networks to select a suitable segmentation method from three methods, i.e., Otsu, HV standard deviation, and Gradient Otsu. The selection is based on the four features extracted from an image including standard deviation of background image, variance coefficient, the ratio of the width to height of both foreground and background histograms. The results show the success of the proposed mechanism. The high segmentation rate reflects the fact that the four carefully selected features are adequate.
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.
References
Ng, H.F.: Automatic Thresholding for Defect Detection. In: Proc. 3rd Int. Conf. Image and Graphics, pp. 532–535 (2004)
Yeh, C., Perng, D.B.: A Reference Standard of Defect Compensation for Leather Transactions. The Int. J. of Advanced Manufacturing Technology 25(11-12), 1197–1204 (2005)
Mery, D., Carrasco, M.: Automated Multiple View Inspection Based on Uncalibrated Image Sequences. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 1238–1247. Springer, Heidelberg (2005)
Cheriet, M., Said, J.N., Suen, C.Y.: A Recursive Thresholding Technique for Image Segmentation. IEEE Trans. Image Processing 7(6), 918–921 (1998)
Chan, F.H.Y., Lam, F.K., Hui, Z.: Adaptive Thresholding by Variational Method. IEEE Trans. Image Processing 7(3), 468–473 (1998)
Mery, D.: Crossing Line Profile: A New Approach to Detecting Defects in Aluminum Die Castings. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 725–732. Springer, Heidelberg (2003)
Gao, H., Siu, W.C., Hou, C.H.: Improved Techniques for Automatic Image Segmentation. IEEE Trans. Circuits Syst. Video Technol. 11(12), 1273–1280 (2001)
Tancharoen, D., Jitapunkul, S., Chompun, S.: Spatial Segmentation based on Modified Morphological Tools. In: Proc. Int. Conf. Information Technology: Coding and Computing, pp. 478–482 (2001)
Bellon, O.R.P., Silva, L.: New Improvements to Range Image Segmentation by Edge Detection. IEEE Signal Processing Lett. 9(2), 43–45 (2002)
Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-time Vision-based System for Textile Fabric Inspection. Real-Time Imaging. 7(6), 507–518 (2001)
Sauvola, J., Pietikainen, M.: Adaptive Document Image Binarization. Pattern Recognition 33, 225–236 (1999)
Otsu, N.: Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man, Cybern. SMC-9(1), 62–66 (1979)
Freeman, J.A., Skapura, D.M.: Neural Networks Algorithms, Applications and Programming Techniques, 1st edn. pp. 89–128. Addison-Wesley, London (1991)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Chiou, YC., Tsai, MR. (2007). Selecting an Appropriate Segmentation Method Automatically Using ANN Classifier. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-73325-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)