A noise tolerant watershed transformation with viscous force for seeded image segmentation

Authors:
Di Yang;Stephen Gould;Marcus Hutter
Affiliations:
Research School of Computer Science, The Australian National University, Australia;Research School of Computer Science, The Australian National University, Australia;Research School of Computer Science, The Australian National University, Australia
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Year:
2012

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Abstract

The watershed transform was proposed as a novel method for image segmentation over 30 years ago. Today it is still used as an elementary step in many powerful segmentation procedures. The watershed transform constitutes one of the main concepts of mathematical morphology as an important region-based image segmentation approach. However, the original watershed transform is highly sensitive to noise and is incapable of detecting objects with broken edges. Consequently its adoption in domains where imaging is subject to high noise is limited. By incorporating a high-order energy term into the original watershed transform, we proposed the viscous force watershed transform, which is more immune to noise and able to detect objects with broken edges.