Temporal Feature Characterization via Dynamic Hidden Markov Tree
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.
Y. H. Zhang et al., "Temporal Feature Characterization via Dynamic Hidden Markov Tree", Applied Mechanics and Materials, Vols. 128-129, pp. 1085-1088, 2012