Paper Title:
Temporal Feature Characterization via Dynamic Hidden Markov Tree
  Abstract

We present a novel multiscale dynamic methodology for automatic machine vision inspection aiming at characterizing temporal features of tobacco leaves. The image sequences of tobacco leaves are transformed from RGB color space to L*a*b* color space, which provides a uniform perceptual difference measure. The image sequences are then represented by a multiscale Dynamic Hidden Markov tree (DHMT), which models not only inter and intra scale dependences of wavelet coefficients, but also temporal dependences of foreground/background observational properties. Experimental results demonstrate temporal consistent mean and covariance values of model coefficients in a* color channel.

  Info
Periodical
Chapter
Chapter 4: Fault Diagnose and Electronmechanical Control
Edited by
Zhixiang Hou
Pages
1085-1088
DOI
10.4028/www.scientific.net/AMM.128-129.1085
Citation
Y. H. Zhang, Z. F. He, Y. S. Zhang, W. Xing, "Temporal Feature Characterization via Dynamic Hidden Markov Tree", Applied Mechanics and Materials, Vols. 128-129, pp. 1085-1088, 2012
Online since
October 2011
Keywords
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$32.00
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