Performance comparison of segmentation algorithms for hand gesture recognition

  • Authors

    • Priyanka Parvathy D Karpagam University, Coimbatore, India
    • Dr Kamalraj Subramaniam Karpagam University, Coimbatore, India
    2018-06-27
    https://doi.org/10.14419/ijet.v7i3.12842
  • Hand Gestures, Preprocessing, Feature Extraction, Edge Segmentation, Non-Local Mean Filtering, Otsu Thresholding, 2d-Discrete Wavelet Transform (Dwt), Particle Swarm Optimization, Artificial Neural Network

  • Abstract

    The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.

     

     

  • References

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  • How to Cite

    Parvathy D, P., & Kamalraj Subramaniam, D. (2018). Performance comparison of segmentation algorithms for hand gesture recognition. International Journal of Engineering & Technology, 7(3), 1227-1232. https://doi.org/10.14419/ijet.v7i3.12842

    Received date: 2018-05-15

    Accepted date: 2018-05-20

    Published date: 2018-06-27