Daniel Schlüter

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We present an integrated approach combining region and contour–based techniques to enhance both segmentation and recognition processes. This cue integration operates on the level of contour–based groups and complete regions, which are matched to reflect a common cause in the image (and thus in the scene). Additionally, we realize a top–down scheme(More)
Both contour and region segmentation have their own advantages and drawbacks. In this work we propose to combine contour approximation and contour-based groupings with region segmentation to enhance both contour-and region-based interpretation of the image data. Contour segments and regions are matched based on the distance between contours and region(More)
We present an integrated approach for contour-based grouping and object recognition. Domain knowledge and domainindependent grouping laws are combined in a multi-layered Markov Random Field framework. It provides a basis for propagating top-down knowledge between different processing cues or input modalities. Additionally, the domain dependent MRF-layer can(More)
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