Edge information based object detection and classification

dc.contributor.advisorAnbarjafari, Gholamrezaen
dc.contributor.advisorRasti, Pejmanen
dc.contributor.authorTarvas, Karlet
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituutet
dc.date.accessioned2016-06-14T12:35:14Z
dc.date.available2016-06-14T12:35:14Z
dc.date.issued2016
dc.description.abstractThis thesis presents work regarding the development a computationally cheap and reliable edge information based object detection and classification system for use on the NAO humanoid robots. The work covers ground detection, edge detection, edge clustering and cluster classification, the latter task being equivalent to object recognition. Numerous novel improvements are proposed, including a new geometric model for ground detection, a joint edge model using two edge detectors in unison for improved edge detection, and a hybrid edge clustering model. Also, a classification model is outlined along with example classifiers and used values. The work is illustrated graphically where applicable.en
dc.identifier.urihttp://hdl.handle.net/10062/51863
dc.language.isoengen
dc.publisherTartu Ülikoolet
dc.subjectEdge detection, clustering, object recognition, computer vision.en
dc.subject.otherbakalaureusetöödet
dc.titleEdge information based object detection and classificationen
dc.title.alternativeÄäreinformatsioonil põhinev esemete tuvastamine ja klassifitseerimineet
dc.typeThesisen

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