20 November 2015 Automatic visual grading of grain products by machine vision
Author Affiliations +
J. of Electronic Imaging, 24(6), 061116 (2015). doi:10.1117/1.JEI.24.6.061116
This paper presents two automatic methods for visual grading, deterministic and probabilistic, designed to solve the industrial problem of evaluation of seed lots from the characterization of a representative sample. The sample is thrown in bulk onto a tray placed in a chamber for acquiring color image in a controlled and reproducible manner. Two image-processing methods have been developed to separate and then characterize each seed present in the image. A shape learning is performed on isolated seeds. Collected information is used for the segmentation. The first approach adopted for the segmentation step is based on simple criteria such as regions, edges, and normals to the boundary. Marked point processes are used in the second approach, leading to tackling of the problem by a technique of energy minimization. In both approaches, an active contour with prior shape is performed to improve the results. A classification is done on shape or color descriptors to evaluate the quality of the sample.
© 2015 SPIE and IS&T
Pierre Dubosclard, Stanislas Larnier, Hubert Konik, Ariane Herbulot, Michel Devy, "Automatic visual grading of grain products by machine vision," Journal of Electronic Imaging 24(6), 061116 (20 November 2015). https://doi.org/10.1117/1.JEI.24.6.061116

Image segmentation


Image processing

Machine vision

Binary data

Stochastic processes

Image processing algorithms and systems

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