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Global Optimized Multiscale Tobacco Leaves Inspection through Graph Cut

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We present a novel multiscale methodology for automatic machine vision application aiming at detecting the size ratio of tobacco leave, and the inspection data will be feedback to adjust running parameters of packaging systems. Firstly, the image is represented by a multiscale Markov Random Field(MRF) model, namely, hidden Markov tree(HMT), which models inter and intra scale dependices of wavelet coefficients. Secondly, according to convex optimization theorem, the energy on hidden Markov tree model is reformulated as a convex version in terms of pseudo marginals. Finally we give the tobacco target segmentation results of the inspection system, which appears to be of better segmentation quality compared to that of the conventional nonconvex energy function.

Document Type: Research Article

Publication date: January 1, 2010

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  • For more than 25 years, NIP has been the leading forum for discussion of advances and new directions in non-impact and digital printing technologies. A comprehensive, industry-wide conference, this meeting includes all aspects of the hardware, materials, software, images, and applications associated with digital printing systems, including drop-on-demand ink jet, wide format ink jet, desktop and continuous ink jet, toner-based electrophotographic printers, production digital printing systems, and thermal printing systems, as well as the engineering capability, optimization, and science involved in these fields.

    Since 2005, NIP has been held in conjunction with the Digital Fabrication Conference.

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