Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems

Automatic visual grading of grain products by machine vision

[+] Author Affiliations
Pierre Dubosclard, Stanislas Larnier, Ariane Herbulot, Michel Devy

CNRS, LAAS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France

Université de Toulouse, UPS, LAAS, F-31400 Toulouse, France

Hubert Konik

Laboratoire Hubert Curien, 18 rue du Professeur Benoît Lauras, 42000 Saint-Etienne, France

J. Electron. Imaging. 24(6), 061116 (Nov 20, 2015). doi:10.1117/1.JEI.24.6.061116
History: Received June 22, 2015; Accepted October 16, 2015
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Abstract.  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

Citation

Pierre Dubosclard ; Stanislas Larnier ; Hubert Konik ; Ariane Herbulot and Michel Devy
"Automatic visual grading of grain products by machine vision", J. Electron. Imaging. 24(6), 061116 (Nov 20, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.061116


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