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Communication Dans Un Congrès Année : 2015

Automated visual grading of grain kernels by machine vision

Résumé

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.
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Dates et versions

hal-01355086 , version 1 (22-08-2016)

Identifiants

Citer

Pierre Dubosclard, Stanislas Larnier, Hubert Konik, Ariane Herbulot, Michel Devy. Automated visual grading of grain kernels by machine vision. 12th International Conference on Quality Control by Artificial Vision 2015, Jun 2015, Le Creusot, France. ⟨10.1117/12.2182811⟩. ⟨hal-01355086⟩
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