Valiente González, JM.; Andreu García, G.; Potter, P.; Rodas Jordá, Á. (2014). Automatic corn (Zea mays) kernel inspection system using novelty detection based on principal component analysis. Biosystems Engineering. 117(1):94-103. doi:10.1016/j.biosystemseng.2013.09.003
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/51683
Title:
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Automatic corn (Zea mays) kernel inspection system using novelty detection based on principal component analysis
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Author:
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Valiente González, José Miguel
Andreu García, Gabriela
Potter, Paulus
Rodas Jordá, Ángel
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UPV Unit:
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Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
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Issued date:
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Abstract:
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[EN] Corn (Zea mays) kernel processing companies evaluate the quality of kernels to determine
the price of a batch. Human inspectors in labs inspect a reduced set of kernels to estimate
the proportion of damaged kernels ...[+]
[EN] Corn (Zea mays) kernel processing companies evaluate the quality of kernels to determine
the price of a batch. Human inspectors in labs inspect a reduced set of kernels to estimate
the proportion of damaged kernels in any given lot. The visual differences between good
and damaged kernels may be minor and, therefore, difficult to discern. Our goal is to design
a computer vision system that enables the automatic evaluation of the quality of corn lots.
To decide if an individual kernel can be accepted or rejected, it is necessary to design a
method to detect defects, as well as quantify the defective proportions. A setup to work inline
and an approach to identify damaged kernels that combines algorithm-based computer
vision techniques of novelty detection and principal component analysis (PCA) is
presented. Experiments were carried out in three colour spaces using 450 dent corn kernels
previously classified by experts. Results show that the method is promising (92% success)
but extensions are recommended to further improve results.
ª 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.
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Subjects:
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Image acquisition system
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Computer vision
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PCA
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Novelty detection
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Copyrigths:
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Cerrado |
Source:
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Biosystems Engineering. (issn:
1537-5110
)
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DOI:
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10.1016/j.biosystemseng.2013.09.003
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.biosystemseng.2013.09.003
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Conference name:
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4th International Workshop on Computer Image Analysis in Agriculture, held at CIGR-AgEng
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Conference place:
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Valencia, Spain
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Conference date:
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July 08-12, 2012
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Thanks:
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We acknowledge the support of the Spanish company DACSA Maiceras Españolas S.A. in supplying the dent corn samples
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Type:
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Artículo
Comunicación en congreso
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