Abstract
This paper deals with hand posture recognition. Thanks to an adequate setup, we afford a database of hand photographs. We propose a novel contour signature, obtained by transforming the image content into several signals. The proposed signature is invariant to translation, rotation, and scaling. It can be used for posture classification purposes. We generate this signature out of photographs of hands: experiments show that the proposed signature provides good recognition results, compared to Hu moments and Fourier descriptors.
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
Bourennane, S., Fossati, C.: Comparison of shape descriptors for hand posture recognition in video. Signal, Image and Video Processing, August 14 (2010) (online First)
Zhu, Y., Xu, G., Kriegman, D.J.: A real-time approach to the spotting, representation, and recognition of hand gestures for Human-Computer Interaction. Computer Vision and Image Understanding 85, 189–208 (2002)
Hu, M.-K.: Visual pattern recognition by moment invariants. IEEE Trans. on Information Theory 8, 179–187 (1962)
Persoon, E., Fu, K.: Shape discrimination using fourier descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(3), 388–397 (1986)
Xu, D., Li, H.: Geometric moment invariants. Pattern Recognition 41, 240–249 (2008)
Charmi, M.-A., Derrode, S., Ghorbel, F.: Fourier-based geometric shape prior for snakes. Pattern Recognition Letters 29(7), 897–904 (2008)
Aghajan, H.K., Kailath, T.: Sensor array processing techniques for super resolution multi-line-fitting and straight edge detection. IEEE Trans. on IP 2(4), 454–465 (1993)
Jiang, H., Marot, J., Fossati, C., Bourennane, S.: Strongly concave star-shaped contour characterization by algebra tools. Elsevier Signal Processing (January 2012)
Shu, X., Wu, X.-J.: A novel contour descriptor for 2D shape matching and its application to image retrieval. Image and Vision Computing 29, 286–294 (2011)
Triesch, J., von der Malsburg, C.: Robust classification of hand postures against complex backgrounds. In: Proc. of the IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 170–175 (1996)
Conseil, S., Bourennane, S., Martin, L.: Comparison of Fourier descriptors and Hu moments for hand posture recognition. In: EUSIPCO 2007, Poznan, Poland (2007)
Caplier, A., Bonnaud, L., Malassiotis, S., Strintzis, M.: Comparison of 2D and 3D analysis for automated cued speech gesture recognition. In: SPECOM (September 2004)
Soriano, M., Martinkauppi, B., Huovinen, S., Laaksonen, M.: Skin detection in video under changing illumination conditions. In: Procs. of the 15th ICPR, vol. 1, pp. 839–842 (2000)
Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. on Systems, Man, and Cybernetics 9, 62–66 (1979)
Amin, M.A., Yan, H.: Sign language finger alphabet recognition from Gabor- PCA representation of hand gestures. In: Proceedings of the 6th International Conference on Machine Learning and Cybernetics, Hong Kong, pp. 2218–2223 (2007)
Huang, D.-Y., Hu, W.-C., Chang, S.-H.: Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination. Expert Systems with Applications 38, 6031–6042 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boughnim, N., Marot, J., Fossati, C., Bourennane, S. (2012). Hand Posture Classification by Means of a New Contour Signature. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-33140-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33139-8
Online ISBN: 978-3-642-33140-4
eBook Packages: Computer ScienceComputer Science (R0)