Ullrich, Torsten ; Fellner, Dieter W. (2011)
Generative Object Definition and Semantic Recognition.
Eurographics 2011 Workshop on 3D Object Retrieval.
doi: 10.2312/3DOR/3DOR11/001-008
Conference or Workshop Item, Bibliographie
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, mark-up and retrieval. In this paper we present a new approach to encode and to extract such semantic information. We use generative modelling 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.).
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2011 |
| Creators: | Ullrich, Torsten ; Fellner, Dieter W. |
| Type of entry: | Bibliographie |
| Title: | Generative Object Definition and Semantic Recognition |
| Language: | English |
| Date: | 2011 |
| Publisher: | Eurographics Association, Goslar |
| Series: | Eurographics Workshop and Symposia Proceedings Series |
| Event Title: | Eurographics 2011 Workshop on 3D Object Retrieval |
| DOI: | 10.2312/3DOR/3DOR11/001-008 |
| 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, mark-up and retrieval. In this paper we present a new approach to encode and to extract such semantic information. We use generative modelling 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.). |
| Uncontrolled Keywords: | Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Business Field: Digital society, Business Field: Virtual engineering, Business Field: Visual decision support, Research Area: Generalized digital documents, Research Area: Semantics in the modeling process, Generative modeling, Object classes, Shape semantics, Digital libraries |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 12 Nov 2018 11:16 |
| Last Modified: | 04 Feb 2022 12:40 |
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