Abstract
Based on the discussion of traditional hand gesture recognition methods and their properties in current studies, analysis the color segmentation, density distribution feature and improved algorithm based on artificial markers in the hand gesture recognition. Then optimize and assembly this series of algorithms, and presented the hand gesture recognition method based on rotation invariance. This method overcomes the shortcomings that the single color models could not recognition complexion-liked area accurately. And this method can realize the multi-angle or posture changing hand gesture recognition in natural state. Using OpenCV which is pragmatic image processing software to design and realize the hand gesture recognition, and present the test results. The results show that the hand gesture recognition method based on rotation invariance has high veracity and real-timing.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cai, L., Xiaojian, L.: A Survey of Gesture Recognition. Journal of Xian University of Arts & Science: Nat. Sci. Ed. 9(2), 91–94 (2006)
Delamarre, Q., Faugeras, O.D.: Finding pose of hand in video images: a stereo based approach. In: Proc. 3rd Int. Con. on Automatic Face and Gesture Recognition, pp. 585–590 (1998)
Yuanxin, Z., Yu, H., Guangyou, X., et al.: Motion-Based segmentation scheme to feature extraction of hand gestures. In: Zhou, J., Jain, A.K., Tianxu, Z., et al. (eds.) Proceedings of SPIE, vol. 3545, pp. 228–231. SPIE, Washington (1998)
Yuanxin, Z., Gangyou, X., Yu, H.: Appearance-Based Dynamic Hand Gesture Recognition from Image Sequences with Complex Background. Journal of Software 11(1), 54–61 (2000)
Garcia, C., Tziritas, G.: Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis. IEEE Trans. on Multimedia 1(3), 264–277 (1999)
Bradski, G., Yeo, B.L., Yeung, M.M.: Gesture for navigation. In: Proceedings of the Storage and Retrieval for Image and Video Databases VII, pp. 230–242 (1999)
Francois, A.R.J., Medioni, G.G.: Adaptive Color Background Modeling for Real-time Segmentation of Video Streams. In: The Proceedings of the International on Imaging Science, Systems and Technology, Las Vegas, Nevada, pp. 227–232 (1999)
Hsu, R., Jain, A.: Face Detection in Color Images. Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)
Tomaz, F., Candeias, T., Shahbazkia, H.: Improved Automatic Skin Detection in Color Images. In: Proceedings of 12th Digital Image Computing: techniques and applications, pp. 10–12 (2003)
Haibing, R., Yuanxin, Z., Guangyou, X.: Hand Gesture Segmentation and Recognition with Complex Backgrounds. Acta Automation Sinica 128(12), 256–261 (2002)
Bobick, A., Davis, J.: Real-time Recognition of Activity Using Temporal Templates. In: IEEE Workshop on Applications of Computer Vision, Sarasota, FL, December 1996, pp. 39–42 (1996)
Wenjun, T., Chengdong, W., Shuying, Z.: Hand Gesture Recognition Based on Fourier Descriptor and BP Neural Network. Journal of Northeastern University: Natural Science 130(9), 1232–1235 (2009)
Yangqing, H., Yuan, G., Linquan, W.: Gesture recognition algorithm based on invariant moment and edge detection. Computer Engineering 31(15), 165–174 (2005)
Paragios, N., Deriche, R.: Geodesic Active Regions and Level Set Methods for Motion Estimation and Tracking. Computer Vision and Image Understanding 97(3), 259–282 (2005)
Vezhnevets, V., Sazonov, V., Andreeva, A.: A Survey on Pixel-based Skin Color Detection Techniques. In: Proc. of GRAPHICON’03, pp. 85–92. [s. n.], Moscow (2003)
Changhua, C., Minchen, Z.: Real-time human face detection and tracking based on HSV model space of skin color. Journal of Fuzhou University: Natural Science Edition 34(6) (2006)
Li, G., Xinghua, S., Yuanyuan, H.: Distance Distribution Histogram and its Application in Trademark Image Retrieval. Journal of Image and Graphics 7(10), 1027–1031 (2002)
Chunmu, H., Lili, Z.: Density Distribution Feature and its Application in Binary Image Retrieval. Journal of Image and Graphics 13(2), 307–311 (2008)
Song, L., Xijian, P., Yihong, D.: Application of Open Source Computer Vision Library. Computer Applications and Software 122(18), 134–136 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, B., Yun, R., Qiu, H. (2010). Hand Gesture Recognition in Natural State Based on Rotation Invariance and OpenCV Realization. In: Zhang, X., Zhong, S., Pan, Z., Wong, K., Yun, R. (eds) Entertainment for Education. Digital Techniques and Systems. Edutainment 2010. Lecture Notes in Computer Science, vol 6249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14533-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-14533-9_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14532-2
Online ISBN: 978-3-642-14533-9
eBook Packages: Computer ScienceComputer Science (R0)