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Journal of Electronic Imaging

Automatic visual grading of grain products by machine vision
Author(s): Pierre Dubosclard; Stanislas Larnier; Hubert Konik; Ariane Herbulot; Michel Devy
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Paper 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.

Paper Details

Date Published: 20 November 2015
PDF: 13 pages
J. Electron. Imaging. 24(6) 061116 doi: 10.1117/1.JEI.24.6.061116
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Pierre Dubosclard, Lab. d'Analyse et d'Architecture des Systèmes (France)
Univ. de Toulouse (France)
Stanislas Larnier, Lab. d'Analyse et d'Architecture des Systèmes (France)
Univ. de Toulouse (France)
Hubert Konik, Lab. Hubert Curien (France)
Ariane Herbulot, Lab. d'Analyse et d'Architecture des Systèmes (France)
Univ. de Toulouse (France)
Michel Devy, Lab. d'Analyse et d'Architecture des Systèmes (France)
Univ. de Toulouse (France)


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