Temporal Feature Characterization via Dynamic Hidden Markov Tree

摘要：

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.

关键词： DHMT multiscale dynamic

作者: Zhang yin-hui He zifen Zhang yunsheng Wu xing

作者单位: Faculty of mechanical and electrical engineering, Kunming University of Science and Technology,650093 Kunming

会议类型: 国际会议

会议名称: 4th International Conference on Measuring Technology and Mechatronics Automation(第四届检测技术与机电自动化国际会议 ICMTMA 2012)

会议地点: 三亚

会议语种:英文

页码: 1085-1088

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）