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Segmentation of Heterogeneous Blob Objects through Voting and Level Set Formulation

Pattern Recognition Letters, in press

    H. Chang
    Q. Yang
    B. Parvin

    ABSTRACT


    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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