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dc.contributor.authorSilva, Carlos
dc.contributor.authorWeber, Juliano
dc.contributor.authorBelloni, Bruno
dc.date.accessioned2020-06-23T11:12:05Z
dc.date.available2020-06-23T11:12:05Z
dc.date.issued2019-03-14
dc.identifier.citationADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8 (2019)
dc.identifier.issn2255-2863
dc.identifier.urihttp://hdl.handle.net/10366/143303
dc.description.abstractThis article presents a hybrid method that uses Convolutional Neural Networks (CNN) to segmentation and Support Vector Machines (SVM) to detection the brandings. The experiments were performed using a cattle branding images. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93% in the first experiment with 39 brandings and 1,950 sample images, and 95% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 32s and 42s, respectively.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherEdiciones Universidad de Salamanca (España)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectComputación
dc.subjectInformótica
dc.subjectComputing
dc.subjectInformation Technology
dc.titleSegmentation and detection of cattle branding images using CNN and SVM classification
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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