EAI Endorsed Transactions on Context-aware Systems and Applications 17(12): e2

Research Article

Context-aware hand poses classifying on images and video-sequences using a combination of wavelet transforms, PCA and neural networks

Download44 downloads
  • @ARTICLE{10.4108/eai.6-7-2017.152758,
        author={Phan Ngoc Hoang and Bui Thi Thu Trang},
        title={Context-aware hand poses classifying on images and video-sequences using a combination of wavelet transforms, PCA and neural networks},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={17},
        number={12},
        publisher={EAI},
        journal_a={CASA},
        year={2017},
        month={7},
        keywords={Hand poses classifying, image processing, video processing, method Viola-Jones, CAMShift algorithm, wavelet transform, PCA, neural networks.},
        doi={10.4108/eai.6-7-2017.152758}
    }
    
  • Phan Ngoc Hoang
    Bui Thi Thu Trang
    Year: 2017
    Context-aware hand poses classifying on images and video-sequences using a combination of wavelet transforms, PCA and neural networks
    CASA
    EAI
    DOI: 10.4108/eai.6-7-2017.152758
Phan Ngoc Hoang1,*, Bui Thi Thu Trang1
  • 1: Ba Ria-Vung Tau University, 80 Truong Cong Dinh street, Ward 3, Vung Tau city, Ba Ria-Vung Tau province, Vietnam
*Contact email: hoangpn285@gmail.com

Abstract

In this paper we propose novel context-aware algorithms for hand poses classifying on images and video-sequences. The proposed hand poses classifying on images algorithm based on Viola-Jones method, wavelet transform, PCA and neural networks. On the first step, the Viola-Jones method is used to find the location of hand pose on images. Then, on the second step, the features of hand pose are extracted using combination of wavelet transform and PCA. Finally, on the last step, these extracted features are classified by multi-layer feed-forward neural networks. The proposed hand poses classifying on video-sequences algorithm based on the combination of CAMShift algorithm and proposed hand poses classifying on images algorithm. The experimental results show that the proposed algorithms effectively classify the hand pose in difference light contrast conditions and compete with state-of-the-art algorithms. [24] Freund Y., Schapire R.E. (1999) A Short Introduction to Boosting // Journal of Japanese Society for Artificial Intelligence, vol.14, no. 5, pp. 771–780 [25] Kim T.K., Wong S.F., Cipolla R.: Cambrige Hand Gesture Data set [Online]. Available: http://www.iis.ee.ic.ac.uk/~tkkim/ges_db.htm. [26] Bradski G. R. (1998) Computer vision face tracking for use in a perceptual user interface // Intel Technology Journal. 1998, 2nd Quarter.