Abstract
Even if lots of object invariant descriptors have been proposed in the literature, putting them into practice in order to obtain a robust system face to several perturbations is still a studied problem. Comparative studies between the most commonly used descriptors put into obviousness the invariance of Zernike moments for simple geometric transformations and their ability to discriminate objects. Whatever, these moments can reveal themselves insufficiently robust face to perturbations such as partial object occultation or presence of a complex background. In order to improve the system performances, we propose in this article to combine the use of Zernike descriptors with a local approach based on the detection of image points of interest. We present in this paper the Zernike invariant moments, Harris keypoint detector and the support vector machine. Experimental results present the contribution of the local approach face to the global one in the last part of this article.
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Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Petrou, M., Kadyrov, A.: Affine Invariant Features from the Trace Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 30–44 (2004)
Khotanzad, A., Hua Hong, Y.: Invariant Image Recognition by Zernike Moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 489–497 (1990)
Chong, C.-W., Raveendran, P., Mukundan, R.: Mean Shift: A Comparative analysis of algorithms for fast computation of Zernike moment. Pattern Recognition 36, 731–742 (2003)
Choksuriwong, A., Laurent, H., Emile, B.: Comparison of invariant descriptors for object recognition. To appear in Proc. of ICIP 2005 (2005)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 1–25 (1995)
http://www1.cs.columbia.edu/cave/research/softlib/coil-100.html
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Choksuriwong, A., Laurent, H., Rosenberger, C., Maaoui, C. (2005). Object Recognition Using Local Characterisation and Zernike Moments. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_14
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DOI: https://doi.org/10.1007/11558484_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
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