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Robust object boundary determination using a locally adaptive level set algorithm

This paper introduces a level set methodology for the precise boundary localization of image objects within an indicated region, designed to be particularly robust against weak or spurious edges, triple points or inhomogeneity of object features in the proximity of the actual interface. The proposed technique requires a reliable classification for a subset of the object interiors, which is propagated towards the unclassified space using a competitive, statistically motivated fast marching region growing algorithm. Color and texture features are used on a locally adaptive, dynamically updated fashion to allow for the robust discrimination of inhomogeneous objects and an efficient implementation. Applications are illustrated in the context of moving object localization and semiautomatic object extraction.