A nonconservative flow field for robust variational image segmentation

Authors:
Pratim Ghosh;Luca Bertelli;Baris Sumengen;B. S. Manjunath
Affiliations:
Vision and Research Laboratory, Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA;Vision and Research Laboratory, Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA;Like.com, San Mateo, CA;Vision and Research Laboratory, Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA
Venue:
IEEE Transactions on Image Processing
Year:
2010

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Abstract

We introduce a robust image segmentation method based on a variational formulation using edge flow vectors. We demonstrate the nonconservative nature of this flow field, a feature that helps in a better segmentation of objects with concavities. A multiscale version of this method is developed and is shown to improve the localization of the object boundaries. We compare and contrast the proposed method with well known state-of-the-art methods. Detailed experimental results are provided on both synthetic and natural images that demonstrate that the proposed approach is quite competitive.