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Proceedings Paper

Automated visual grading of grain kernels 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, 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 tackle the problem by a technique of energy minimization. In both approaches, an active contour with shape prior 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: 30 April 2015
PDF: 8 pages
Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340H (30 April 2015); doi: 10.1117/12.2182793
Show Author Affiliations
Pierre Dubosclard, LAAS, CNRS, Univ. de Toulouse (France)
Stanislas Larnier, LAAS, CNRS, Univ. de Toulouse (France)
Hubert Konik, Lab. Hubert Curien, CNRS (France)
Ariane Herbulot, LAAS, CNRS, Univ. de Toulouse (France)
Michel Devy, LAAS, CNRS, Univ. de Toulouse (France)


Published in SPIE Proceedings Vol. 9534:
Twelfth International Conference on Quality Control by Artificial Vision 2015
Fabrice Meriaudeau; Olivier Aubreton, Editor(s)

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