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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/71974
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dc.contributor.authorLulio, Luciano C.-
dc.contributor.authorTronco, Mario L.-
dc.contributor.authorPorto, Arthur J. V.-
dc.date.accessioned2014-05-27T11:24:50Z-
dc.date.accessioned2016-10-25T18:30:23Z-
dc.date.available2014-05-27T11:24:50Z-
dc.date.available2016-10-25T18:30:23Z-
dc.date.issued2010-11-29-
dc.identifierhttp://dx.doi.org/10.1109/ICMA.2010.5588694-
dc.identifier.citation2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010, p. 1771-1776.-
dc.identifier.urihttp://hdl.handle.net/11449/71974-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/71974-
dc.description.abstractThe main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.en
dc.format.extent1771-1776-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectArtificial neural networks-
dc.subjectComputer vision-
dc.subjectImage recognition and processing-
dc.subjectMobile robots-
dc.subjectApplication area-
dc.subjectArtificial Neural Network-
dc.subjectComputational platforms-
dc.subjectHSV space-
dc.subjectMobile Robot Navigation-
dc.subjectMulti layer perceptron-
dc.subjectNavigation problem-
dc.subjectOmnidirectional vision system-
dc.subjectRecognition methods-
dc.subjectSegmentation techniques-
dc.subjectSegmented images-
dc.subjectSIMULINK environment-
dc.subjectStatistical images-
dc.subjectBackpropagation algorithms-
dc.subjectImage recognition-
dc.subjectImage segmentation-
dc.subjectMechatronics-
dc.subjectNavigation-
dc.subjectNeural networks-
dc.subjectWireless networks-
dc.titleANN statistical image recognition method for computer vision in agricultural mobile robot navigationen
dc.typeoutro-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationMechanical Engineering Department Engineering School of Sao Carlos University of Sao Paulo, CEP 13566-590-
dc.description.affiliationDepartment of Computer Science and Statistics State University of Sao Paulo, CEP 15054-000-
dc.description.affiliationUnespDepartment of Computer Science and Statistics – UNESP, IBILCE-
dc.identifier.doi10.1109/ICMA.2010.5588694-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartof2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010-
dc.identifier.scopus2-s2.0-78649235689-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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