Generative Object Definition and Semantic Recognition

dc.contributor.authorUllrich, Torstenen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorH. Laga and T. Schreck and A. Ferreira and A. Godil and I. Pratikakis and R. Veltkampen_US
dc.date.accessioned2013-04-24T13:02:54Z
dc.date.available2013-04-24T13:02:54Z
dc.date.issued2011en_US
dc.description.abstract''What is the difference between a cup and a door?'' These kinds of questions have to be answered in the context of digital libraries. This semantic information, which describes an object on a high, abstract level, is needed in order to provide digital library services such as indexing, markup and retrieval. In this paper we present a new approach to encode and to extract such semantic information. We use generative modeling techniques to describe a class of objects: each class is represented by one algorithm; and each object is one set of high-level parameters, which reproduces the object if passed to the algorithm. Furthermore, the algorithm is annotated with semantic information, i.e. a human-readable description of the object class it represents. We use such an object description to recognize objects in real-world data e.g. laser scans. Using an algorithmic object description, we are able to identify 3D subparts, which can be described and generated by the algorithm. Furthermore, we can determine the needed input parameters. In this way, we can classify objects, recognize them semantically and we can determine their parameters (cup's height, radius, etc.).en_US
dc.description.sectionheaders3D Recognitionen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-31-6en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR11/001-008en_US
dc.publisherThe Eurographics Associationen_US
dc.titleGenerative Object Definition and Semantic Recognitionen_US
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