Stephan Tschechne

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Model-based methods in computer vision have proven to be a good approach for compressing the large amount of information in images. Fitting algorithms search for those parameters of the model that optimise the objective function given a certain image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective(More)
Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting(More)
The analysis of affective or communicational states in human-human and human-computer interaction (HCI) using automatic machine analysis and learning approaches often suffers from the simplicity of the approaches or that very ambitious steps are often tried to be taken at once. In this paper, we propose a generic framework that overcomes many difficulties(More)
Computational models of visual processing often use framebased image acquisition techniques to process a temporally changing stimulus. This approach is unlike biological mechanisms that are spikebased and independent of individual frames. The neuromorphic Dynamic Vision Sensor (DVS) [Lichtsteiner et al., 2008] provides a stream of independent visual events(More)
Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local(More)
Event-based vision sensors sample individual pixels at a much higher temporal resolution and provide a representation of the visual input available in their receptive fields that is temporally independent of neighboring pixels. The information available on pixel level for subsequent processing stages is reduced to representations of changes in the local(More)
Non-verbal communication signals are to a large part conveyed by visual motion information of the user’s facial components (intrinsic motion) and head (extrinsic motion). An observer perceives the visual flow as a superposition of both types of motions. However, when visual signals are used for training of classifiers for non-articulated communication(More)