3D object recognition with a specialized mixtures of experts architecture
- Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
| Verfasserangaben: | Peter Walter, Ingo ElsenORCiD, Holger Müller, Karl-Friedrich Kraiss |
|---|---|
| DOI: | https://doi.org/10.1109/IJCNN.1999.836243 |
| ISBN: | 0-7803-5529-6 |
| ISSN: | 1098-7576 |
| Titel des übergeordneten Werkes (Englisch): | IJCNN'99. International Joint Conference on Neural Networks. Proceedings |
| Verlag: | IEEE |
| Verlagsort: | New York |
| Dokumentart: | Konferenzveröffentlichung |
| Sprache: | Englisch |
| Erscheinungsjahr: | 1999 |
| Datum der Publikation (Server): | 12.04.2023 |
| Umfang: | 6 Seiten |
| Erste Seite: | 3563 |
| Letzte Seite: | 3568 |
| Bemerkung: | Washington, DC 10-16.07.1999 |
| Link: | https://doi.org/10.1109/IJCNN.1999.836243 |
| Zugriffsart: | campus |
| Fachbereiche und Einrichtungen: | FH Aachen / ECSM European Center for Sustainable Mobility |
| FH Aachen / Fachbereich Elektrotechnik und Informationstechnik | |
| collections: | Verlag / IEEE |
