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Zero-crossing rates of wavelet frame representations for texture classification

Zero-crossing rates of wavelet frame representations for texture classification

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The zero-crossing rates (ZCRs) of the wavelet frame representation of texture images are proposed as features for texture classification. Results indicate that ZCR features lower the probability of error (PE) by 40% over energy (EN) and correlation coefficient features. Augmenting ZCR features with EN features improves the PE by 70% over ZCR features alone.

References

    1. 1)
      • Image processing, analysis, and machine vision
    2. 2)
      • Statistical texture characterization from discrete wavelet representations
    3. 3)
      • Van Nevel, A.: `Texture classification using wavelet frame decompositions', Proc. 31st Asilomar Conf. Signals, Systems and Computers, November 1997, Pacific Grove, CA, USA, 1, p. 311–314
    4. 4)
      • Time series analysis by higher order crossings
    5. 5)
      • Nonparametrics statistical inference
    6. 6)
      • http://vismod.www.media.mit.edu/vismod/imagery/VisionTexture/
    7. 7)
      • Image coding using wavelet transform
    8. 8)
      • Introduction to statistical pattern recognition
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