Abstract
The segmentation of foreground silhouettes of humans in camera images is a fundamental step in many computer vision and pattern recognition tasks. We present an approach which, based on color distributions, estimates the foreground by automatically integrating data driven 3d scene knowledge from multiple static views. These estimates are integrated into a level set approach to provide the final segmentation results. The advantage of the presented approach is that ambiguities based on color distributions of the fore- and background can be resolved in many cases utilizing the integration of implicitly extracted 3d scene knowledge and 2d boundary constraints. The presented approach is thereby able to automatically handle cluttered scenes as well as scenes with partially changing backgrounds and changing light conditions.
This work was partially supported by a grant from the Ministry of Science, Research and the Arts of Baden-Württemberg.
This work is partially funded by the German Research Foundation(RO 2497/6-1).
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Feldmann, T., Scheuermann, B., Rosenhahn, B., Wörner, A. (2010). N-View Human Silhouette Segmentation in Cluttered, Partially Changing Environments ,. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_37
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DOI: https://doi.org/10.1007/978-3-642-15986-2_37
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