Abstract
A novel variational level set method for multiple object detection is presented, which uses n-1 level set functions for n-1 objects and the background without overlapping and vacuum problems. The energy functional includes three parts. The first part is a parametric region-based model via generic image noise distributions, the second part is the classic edge-based model, the third part is a term used to enforce the constraints of level set functions as signed distance functions. Characteristic functions for region partitioning are written in a unified form using Heaviside functions of level set functions. Some intermediate terms in evolution equations are extracted in a unified form for simplification of expressions and computation efficiency. The corresponding semi-implicit schemes are derived and used to some examples for segmentation of synthetic and real images to validate the method suggested in this paper.
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Pan, Z., Li, H., Wei, W., Xu, S. (2008). A Variational Level Set Method for Multiple Object Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_72
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DOI: https://doi.org/10.1007/978-3-540-89646-3_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89645-6
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