The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Real-time Vision-based Hand Gesture Recognition Using Haar-like Features
Abstract
This paper proposes a two level approach to solve the problem of real-time vision-based hand gesture classification. The lower level of the approach implements the posture recognition with Haar-like features and the AdaBoost learning algorithm. With this algorithm, real-time performance and high recognition accuracy can be obtained. The higher level implements the linguistic hand gesture recognition using a context-free grammar-based syntactic analysis. Given an input gesture, based on the extracted postures, the composite gestures can be parsed and recognized with a set of primitives and production rules.
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Citations
Cites
An extended set of Haar-like features for rapid object detection
Lienhart, R., & Maydt, J. (2003) · Proceedings. International Conference on Image Processing · doi:10.1109/icip.2002.1038171 · Cited by 2,977 · cited 2×
“1 shows the extended Haar-like features set proposed by Lienhart and Maydt [8].”
Background
Cited by
Vision Based Hand Gesture Recognition
Garg, P., Aggarwal, N., & Sofat, S. (2009) · doi:10.5281/zenodo.1074854 · Cited by 275
Metadata
- Authors
- Chen, Q., Georganas, N. D., & Petriu, E. M.
- Venue
- 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007
- Publisher
- IEEE
- Published
- 2007-05
- Pages
- 1-6
- ISSN
- 1091-5281 (ISSN-L)
- OpenAlex
- W2164416728
- Cited by
- 302
- References
- 1
- FWCI
- 10.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, Q., Georganas, N. D., & Petriu, E. M. (2007). Real-time Vision-based Hand Gesture Recognition Using Haar-like Features. 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007. https://doi.org/10.1109/imtc.2007.379068
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