Precise head segmentation on arbitrary backgrounds

Authors:
David C. Schneider;Benjamin Prestele;Peter Eisert
Affiliations:
Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, Berlin, Germany;Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, Berlin, Germany;Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, Berlin, Germany
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

We propose a method for segmentation of frontal human portraits from arbitrary unknown backgrounds. Semantic information is used to project the face into a normalized reference frame. A shape model learned from a set of manually segmented faces is used to compute a rough initial segmentation using a fast iterative algorithm. The rough initial cutout is refined with a boundary based algorithm called "Cluster Cutting". Cluster Cutting uses a cost function derived from clustering pixels along the normal of the initial segmentation path with a tree-building algorithm. The result can be refined by the user with an interactive variant of the same algorithm.