Abstract
We present a design methodology for automatic machine vision application aiming at detecting the size ratio of tobacco leaves which will be feedback to adjust running parameters of manufacture system. Firstly, the image is represented by Markov Random Field(MRF) model which consists of a label field and an observation field. Secondly, according to Bayes theorem, the segmentation problem is translated into Maximum a Posteriori(MAP) estimation of the label field and the estimation problem is solved by Iterated Conditional Model(ICM) algorithm. Finally we give the setup of the inspection system and experimented with a real-time image acquired from it, the experiment shows better detection results than Otsu’s segmentation method especially in the larger leaf regions.
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Zhang, Y., Zhang, Y., He, Z., Tang, X. (2007). Automatic Inspection of Tobacco Leaves Based on MRF Image Model. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_65
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DOI: https://doi.org/10.1007/978-3-540-76856-2_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76855-5
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