Segmentation of Heterogeneous Blob Objects through Voting and Level Set
Pattern Recognition Letters, in press
Blob-like structures occur often in nature, where they aid in
cueing and the pre-attentive process.
These structures often overlap, form perceptual boundaries, and are heterogenous in shape, size, and
intensity. In this paper, voting, Voronoi tessellation, and level set methods ar
e combined to delineate blob-like structures. Voting and subsequent Voronoi tessellation pr
ovide the initial condition and the boundary constraints for each blob, while curve evolution thro
ugh level set formulation provides refined segmentation of each blob within the Voronoi re
gion. The paper concludes with the application of the proposed method
to a dataset derived from biological imaging assays and stellar data.
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