G
Gerhard Sagerer
Researcher at Bielefeld University
Publications - 281
Citations - 5817
Gerhard Sagerer is an academic researcher from Bielefeld University. The author has contributed to research in topics: Mobile robot & Social robot. The author has an hindex of 37, co-authored 281 publications receiving 5585 citations. Previous affiliations of Gerhard Sagerer include University of Erlangen-Nuremberg & Daimler AG.
Papers
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Proceedings Article
Integrated Recognition and Interpretation of Speech for a Construction Task Domain
TL;DR: This paper presents a speech recogniser that uses an acoustic model as well as a statistical language model, which are both generated from a corpus, and a speech understanding module that tries to extract an internal representation of the meaning of the utterance.
Proceedings ArticleDOI
Incremental generation of word graphs
TL;DR: An algorithm for the incremental generation of word graphs is presented that can be used for early interaction between linguistic analysis and acoustic recognition and derive acoustic constraints from linguistic restrictions dynamically.
Journal Article
Towards an Image Understanding Architecture for a Situated Artificial Communicator
Christian Bauckhage,Gernot A. Fink,Gunther Heidemann,Nils Jungclaus,Franz Kummert,Stefan Posch,Helge Ritter,Gerhard Sagerer,Daniel Schlüter +8 more
TL;DR: In this article, the authors proposed an image understanding system for communicating with human-machine interaction, where image processing is initially carried out in separate pathways using different schemes of image segmentation, followed by a hybrid technique for 2D-object recognition.
Book ChapterDOI
Erkennung von Konstruktionshandlungen aus Bildfolgen
TL;DR: Ein bildbasierter Ansatz zur Analyse von Konstruktionshandlungen vorgestellt, in which mehrere Aktionshypothesen gleichzeitig verfolgt sowie probabilistische Informationen uber Aktionsfolgen in den Klassifikationsprozes integriert werden.
Book ChapterDOI
Werkzeuge zur Modellgesteuerten Bildanalyse und Wissensakquisition
TL;DR: Im folgenden Artikel wird ein Systemrahmen fur derartige Anwendungen vorgestellt, der auch eine Wissensakquisitionskomponente auf CAD-Daten basierend umfast gefunden.