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Content archived on 2024-05-07

Neural architecture for dynamic gesture recognition

Objective



Research objectives and content
Gestures are part of everyday natural human cornmunication. They are used as an accompaniment to spoken language and as an expressive medium in their own right. Recently, there have been strong efforts to develop natural, intelligent interfaces between users and systems based on gesture recognition, which can be used easily and intuitively. In the project GESTIK at the Technical University of Ilmenau for example, is currently working in the field of static gestures. The main goal of the research project is to use the motion information obtained from image sequences for gesture-based human-machine-interaction. One part of the project will deal with neural mechanisms for motion-based saliency to support the robust detection of a potential user in an unknown operational area and to complement the existing neural architecture of the project GESTIK. The second and main part of the proposal is to deal with the motion-based description and recognition of dynamic gestures to extend the gesture-based interaction of the project GESTIK from static to dynamic gestures. A neural architecture has to be designed and implemented that is capable to obtain a sufficient, gesture-relevant description of the images within the (video) sequence, and to represent the current dynamic gesture in such a way that an efficient matching against stored gesture instances can be made.
Training content (objective, benefit and expected impact)
During the past six months funded by ,,Provincia Autonoma di Trento' I have laid a solid basis of my knowledge in the field of Computer Vision and Neural Networks. Based on this preparation the proposed project will lead a PhD in Neuroinformatics. In addition, the collaboration with excellent scientist such as Prof. H.-M. Gross and his colleagues of the Department of Neuroinformatics at the Technical University of Ilmenau will increase my experience und skills. Concluding, the training will lead to a very good foundation af an academic career in the field of Neural Networks and Computer Vision.
Links with industry / industrial relevance (22)
On long term the aspected results of this project can be applied in industrial area as well, e.g. human-machine-interaction and robot vision.

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Topic(s)

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Funding Scheme

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RGI - Research grants (individual fellowships)

Coordinator

Technische Universität Ilmenau
EU contribution
No data
Address
Am Ehrenberg
98693 Ilmenau
Germany

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Total cost

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Participants (1)

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