Abstract
We present a novel way to use the Scale Invariance Feature Transform (SIFT) on binary images. As far as we know, we proposed employ SIFT on binary images for hand gesture recognition and provide more accurate result comparing to traditional template approaches. There exist many restrictions on template matching approaches, such as the rotation must be less than 15°, and the variation on scale, etc. However, our proposed approach is robust against rotations, scaling, illumination conditions, and can recognize hand gestures in real-time with only off-the-shelf camera such as webcams. The proposed approach employs the SIFT features on binary image, the k-means clustering to map keypoints into a unified dimensional histogram vector (bag-of-words), and the Support Vector Machine (SVM) to classify different hand gestures.
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References
Dardas, N.H., Georganas, N.D.: Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques. IEEE Transaction on Instrumentation and Measurement (November 2011)
Stenger, B.: Template-Based Hand Pose Recognition Using Multiple Cues. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 551–560. Springer, Heidelberg (2006)
Tofighi, G., Monadjemi, S.A., Ghasem-Aghaee, N.: Rapid Hand Posture Recognition Using Adaptive Histogram Template of Skin and Hand Edge Contour. In: 2010 6th Iranian Machine Vision and Image Processing (MVIP) (October 2010)
Kanungo, T., Mount, D.M., Netanyahu, N., Piatko, C., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Analysis and Machine Intelligence (2002)
Van den Bergh, M., Van Gool, L.: Combining RGB and ToF Cameras for Real-time 3D Hand Gesture Interaction. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV) (January 2011)
Xu, Z., Xiang, C., Wen-hui, W., Ji-hai, Y., Vuokko, L., Kong-qiao, W.: Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors. In: Proceedings of the 14th International Conference on Intelligent User Interfaces (2009)
Weston, J., Watkins, C.: Support vector machines for multi-class pattern recognition. In: Proceedings of European Symposium on Artificial Neural Networks, Bruges, Belgium (April 1999)
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks (March 2002)
Friedman, J.H.: Another approach to polychotomous classification. Department of Statistics and Stanford Linear Accelerator Center Stanford University (1997)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (November 2004)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)
Jiang, Y., Ngo, C., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the ACM International Conference on Image and Video (2007)
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Lin, WS., Wu, YL., Hung, WC., Tang, CY. (2013). A Study of Real-Time Hand Gesture Recognition Using SIFT on Binary Images. In: Pan, JS., Yang, CN., Lin, CC. (eds) Advances in Intelligent Systems and Applications - Volume 2. Smart Innovation, Systems and Technologies, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35473-1_24
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DOI: https://doi.org/10.1007/978-3-642-35473-1_24
Publisher Name: Springer, Berlin, Heidelberg
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