Precise head segmentation on arbitrary backgrounds

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.

• Type: Journal
• Year: 2009
• Where: ICIP
• Authors: David C. Schneider, Benjamin Prestele, Peter Eisert
• Keywords: Algorithm, ICIP 2009, Image Processing, Initial Segmentation, Rough Initial Segmentation