Abstract
We propose a probabilistic segmentation scheme, which is widely applicable to some extend. Besides the segmentation itself our model incorporates object specific shading. Dependent upon application, the latter is interpreted either as a perturbation or as meaningful object characteristic. We discuss the recognition task for segmentation, learning tasks for parameter estimation as well as different formulations of shading estimation tasks. © 2008 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Schlesinger, D., & Flach, B. (2008). A probabilistic segmentation scheme. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5096 LNCS, pp. 183–192). https://doi.org/10.1007/978-3-540-69321-5_19
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