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Computer-based detection and classification of flaws in citrus fruits

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Computer-based detection and classification of flaws in citrus fruits

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dc.contributor.author López Monfort, José Javier es_ES
dc.contributor.author Cobos Serrano, Máximo es_ES
dc.contributor.author Aguilera Martí, Emanuel es_ES
dc.date.accessioned 2018-04-28T04:14:09Z
dc.date.available 2018-04-28T04:14:09Z
dc.date.issued 2011 es_ES
dc.identifier.issn 0941-0643 es_ES
dc.identifier.uri http://hdl.handle.net/10251/101127
dc.description.abstract [EN] In this paper, a system for quality control in citrus fruits is presented. In current citrus manufacturing industries, calliper and color are successfully used for the automatic classification of fruits using vision systems. However, the detection of flaws in the citrus surface is carried out by means of human inspection. In this work, a computer vision system capable of detecting defects in the citrus peel and also classifying the type of flaw is presented. First, a review of citrus illnesses has been carried out in order to build a database of digitalized oranges classified by the kind of fault, which is used as a training set. The segmentation of faulty zones is performed by applying the Sobel gradient to the image. Afterwards, color and texture features of the flaw are extracted considering different color spaces, some of them related to high order statistics. Several techniques have been employed for classification purposes: Euler distance to a prototype, to the nearest neighbor and k-nearest neighbors. Additionally, a three layer neural network has been tested and compared, obtaining promising results. es_ES
dc.language Inglés es_ES
dc.publisher SPRINGER es_ES
dc.relation.ispartof Neural Computing and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Computer vision es_ES
dc.subject Automatic inspection system es_ES
dc.subject Texture analysis segmentation es_ES
dc.subject Quality control es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Computer-based detection and classification of flaws in citrus fruits es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00521-010-0396-2 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation López Monfort, JJ.; Cobos Serrano, M.; Aguilera Martí, E. (2011). Computer-based detection and classification of flaws in citrus fruits. Neural Computing and Applications. 20(7):975-981. doi:10.1007/s00521-010-0396-2 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s00521-010-0396-2 es_ES
dc.description.upvformatpinicio 975 es_ES
dc.description.upvformatpfin 981 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 7 es_ES
dc.relation.pasarela S\213783 es_ES
dc.relation.references Blasco J, Aleixos J, Molto E (2007) Computer vision detection of peel defects in citrus by means of a region oriented segmentation. J Food Eng 81:535–543 es_ES
dc.relation.references Blasco J, Aleixos N, Gomez J, Molto E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83:384–391 es_ES
dc.relation.references Bryson AE, Ho YC (1969) Applied optimal control: optimization, estimation, and control. Xerox College Publishing, Lexington, MA es_ES
dc.relation.references Conners RWea (1983) Identifying and locating surface defects in wood. IEEE Trans Pattern Anal Mach Intell 5:573–583 es_ES
dc.relation.references Diaz R, Gil L, Serrano C, Blasco M, Molto E, Blasco J (2004) Comparison of three algorithms in the classification of table olives by means of computer vision. J Food Eng 61:101–107 es_ES
dc.relation.references Douglas DH, Peucker TK (1973) Algorithm for the reduction of the number of points required to represent a line or its caricature. The Can Cartogr 10(2):112–122 es_ES
dc.relation.references Du CJ, Sun DW (2005) Comparison of three methods for classification of pizza topping using different colour space transformations. J Food Eng 68:277–287 es_ES
dc.relation.references Kolesnikov A (2003) Efficient algorithms for vectorization and polygonal approximation. Ph.D. thesis, University of Joensuu, Finland es_ES
dc.relation.references Molto E (1997) A computer vision system for inspecting citrus, peaches and apples. In: Proceedings of VII national symposium on pattern recognition and image analysis. Sabadell, Spain, pp 121–126 es_ES
dc.relation.references Muir AY, Porteus RL, Wastie RL (1982) Experiments in the detection of incipient diseases in potato tubers by optical methods. J Agric Eng Res 27:131–138 es_ES
dc.relation.references Q Li (2002) Computer vision based system for apple surface defect detection. computer and electronics in agriculture. Comput Electron Agric 36:215–223 es_ES
dc.relation.references Ruiz LA, Molto E, Juste F, Pla F, Valiente R (1996) Location and characterization of the stem–calyx area on oranges by computer vision. J Agric Eng Res 64:165–172 es_ES
dc.relation.references Tan TSC, Kittler J (1994) Colour texture analysis using colour histogram. IEEE Proc Vis Image Signal Process 141:403–412 es_ES
dc.relation.references Wen Z, Tao Y (1999) Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines. Expert Syst Appl 16:307–313 es_ES


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