2,781 research outputs found
A speech understanding and dialog system with a homogeneous linguistic knowledge base
No full textMast M, Kummert F, Ehrlich U, et al. A speech understanding and dialog system with a homogeneous linguistic knowledge base. IEEE transactions on pattern analysis and machine intelligence. 1994;16(2):179-194.This article presents the speech understanding and dialog system EVAR. All levels of linguistic knowledge are used both to control the analysis process and for the interpretation of an utterance. All kinds of knowledge are integrated in a homogeneous knowledge base. The control algorithm used for the analysis is defined within the representation scheme and does not depend on the application. One of the aims of EVAR is to develop a system structure where linguistic and non-linguistic expectations could be used not only for the interpretation but also as predictions for the recognition process
ERNEST: a semantic network system for pattern understanding
No full textNiemann H, Sagerer G, Schröder S, Kummert F. ERNEST: a semantic network system for pattern understanding. IEEE transactions on pattern analysis and machine intelligence. 1990;12(9):883-905
An incremental approach to automated protein localisation
No full textTscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background:
The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected.
Results:
We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided.
Conclusion:
We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments
Interpretation von Bild- und Sprachsignalen: ein hybrider Ansatz
No full textKummert F. Interpretation von Bild- und Sprachsignalen: ein hybrider Ansatz. Aachen: Shaker; 1998
Flexible Steuerung eines sprachverstehenden Systems mit homogener Wissensbasis
No full textKummert F. Flexible Steuerung eines sprachverstehenden Systems mit homogener Wissensbasis. Dissertationen zur Künstlichen Intelligenz ; 12. Sankt Augustin: Infix; 1992
Collaboration in Human-Computer Communication
No full textKronenberg S, Kummert F. Collaboration in Human-Computer Communication. In: 3rd International Workshop on Human-Computer Conversation. 2000: 87-92
Dynamic dialog system for human robot collaboration
No full textKipp A, Kummert F. Dynamic dialog system for human robot collaboration. In: Proceedings of the second international conference on Human-agent interaction - HAI '14. 2014: 225-228
"I know how you performed!"
No full textKipp A, Kummert F. "I know how you performed!". In: Proceedings of the Fourth International Conference on Human Agent Interaction - HAI '16. New York, NY: Association for Computing Machinery (ACM); 2016: 1
Exercising with a Humanoid Companion is More Effective than Exercising Alone
No full textSchneider S, Kummert F. Exercising with a Humanoid Companion is More Effective than Exercising Alone. In: Humanoids 2016 : IEEE-RAS International Conference on Humanoid Robots. Piscataway, NJ: IEEE; 2016: 495-501
Towards Addressee Recognition in Smart Robotic Environments An Evidence Based Approach
No full textRichter V, Kummert F. Towards Addressee Recognition in Smart Robotic Environments An Evidence Based Approach. In: Proceedings of the 1st Workshop on Embodied Interaction with Smart Environments - EISE '16. New York, NY: Association for Computing Machinery (ACM); 2016: 1
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