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dc.contributor.advisorSierra Araujo, Basilio ORCIDes
dc.contributor.authorLassalle, Pierre
dc.contributor.otherCiencia de la Computación e Inteligencia Artificial/Konputazio Zientzia eta Adimen Artifiziala
dc.date.accessioned2013-06-12T07:36:38Z
dc.date.available2013-06-12T07:36:38Z
dc.date.issued2013-06-12T07:36:38Z
dc.identifier.urihttp://hdl.handle.net/10810/10233
dc.description.abstractThis project introduces an improvement of the vision capacity of the robot Robotino operating under ROS platform. A method for recognizing object class using binary features has been developed. The proposed method performs a binary classification of the descriptors of each training image to characterize the appearance of the object class. It presents the use of the binary descriptor based on the difference of gray intensity of the pixels in the image. It shows that binary features are suitable to represent object class in spite of the low resolution and the weak information concerning details of the object in the image. It also introduces the use of a boosting method (Adaboost) of feature selection al- lowing to eliminate redundancies and noise in order to improve the performance of the classifier. Finally, a kernel classifier SVM (Support Vector Machine) is trained with the available database and applied for predictions on new images. One possible future work is to establish a visual servo-control that is to say the reac- tion of the robot to the detection of the object.es
dc.language.isoenges
dc.relation.ispartofseries2012-4;4
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/*
dc.subjectbinary descriptores
dc.subjectfeature selectiones
dc.subjectsupervised classificationes
dc.subjectobject recognitiones
dc.titleStudy of an object recognition algorithmes
dc.typeinfo:eu-repo/semantics/masterThesises
dc.rights.holderAttribution-NonCommercial-NoDerivs 3.0 Unported*


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported