Daniel Schlüter

Learn More
To overcome fragmentation of an initial contour-based segmentation and to organize contour segments into image primitives on a higher level of abstraction, regularities of the image data are exploited using ideas from the Gestalt psychology. First, groups are hypothesized within a hierarchy based on local evidence only, where the criteria are derived from a(More)
We present an integrated approach combining region and contour–based techniques to enhance both segmenta-tion 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 domain-independent 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(More)
  • 1