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AuthorSergeev, Nikolaidc.contributor.author
Date of accession2016-03-15T09:03:37Zdc.date.accessioned
Available in OPARU since2016-03-15T09:03:37Zdc.date.available
Year of creation2012dc.date.created
AbstractIn this thesis a new approach to object recognition will be introduced. The basic idea of the approach is to compare two combinations of half ellipses representing two objects in order to find out whether the combinations can be at least partially transformed into each other by an affine mapping. As the transformation can be partial the system is robust to partial occlusion. The transformation does not have to be exact. Two combinations being able to be transformed into each other approximately with an error up to some epsilon-bound are regarded as similar. The epsilon-error tolerance makes the system robust to deformation. Further attractive properties of the approach are: its capability of stable separation of a single object from its background or of several objects partially occluding each other from one another, its capability to learn a new object in a time independent of the number of objects already learned, color information can be ignored or combined with form representation. Additionally a new machine learning algorithm developed to handle the representations based on half ellipses will be introduced. Because of the abstract task that it solves, the algorithm can be characterized as "near neighbor search". It looks for all stored vectors which are similar enough to the query vector with respect to the maximum norm. It is significantly responsible for many properties of the approach.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordObject recognitiondc.subject
KeywordObject representationdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
MeSHMan-machine systemsdc.subject.mesh
TitleA new approach to object recognitiondc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-2424dc.identifier.doi
PPN725586788dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-81094dc.identifier.urn
GNDObjekterkennungdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2012-07-27T10:00:39Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionW: W-H 13.043uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS ID8109uulm.vtsID
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie


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