Abstract
This paper presents a genetic algorithm (GA) based segmentation technique that can separate two touching objects intended for an automatic recognition of plastic bottles moving on a conveyor belt. The proposed method is based on the possibility to separate the two objects by means of a straight line, whose position is determined by a GA. Extensive testing shows that the proposed method is fast and yields high success rate of correct segmentation with only a limited number of both chromosomes and iterations.
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References
Pham, D., Alcock, R.: Smart Inspection Systems. Academic Press, San Diego (2003)
Haralick, R.M., Shapiro, L.G.: Survey: Image Segmentation Techniques. Comp. Vision Graph. Image Process. 29, 100–132 (1985)
Melkemi, K.E., et al.: A Multiagent System Approach for Image Segmentation Using GA Algorithms and External Optimization Heuristics. Pattern Recognit. Lett. 27, 1230–1238 (2006)
Mital, A., et al.: A Comparison Between Manual and Hybrid Methods in Parts Inspection. Integrated Manufacturing Systems 9, 344–349 (1998)
Wahab, D.A., et al.: Development of a Prototype Automated Sorting System for Plastic R cycling. Am. J. Appl. Sci. 3(7), 1924–1928 (2006)
Haris, K., et al.: Hybrid Image Segmentation Using Watersheds and Fast Region Merging. IEEE Trans. Image Process. 7, 1684–1699 (1998)
Goldberg, D.E.: Genetic Algorithm. In: Search, Optimization and Machine Learning, pp. 28–60. Addison-Wesley, Reading MA (1989)
Koza, J.R.: Survey of Genetic Algorithms and Genetic Programming. In: Proc. of the We con95-Conf. Record: Microelectronics, Communications Technology, Producing Quality Products, Mobile and Portable Power, Emerging Technologies, San Francisco CA (1995)
Yoshimura, M., Oe, S.: Evolutionary Segmentation of Texture Image Using Genetic Algorithms towards Automatic Decision of Optimum Number of Segmentation Areas. Pattern Recognit. 32, 2051–2054 (1999)
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Scavino, E., Abdul Wahab, D., Basri, H., Mustafa, M.M., Hussain, A. (2007). An Efficient Segmentation Technique for Known Touching Objects Using a Genetic Algorithm Approach. 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_93
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DOI: https://doi.org/10.1007/978-3-540-76928-6_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
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