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We present a method for segmenting 2D microscopy images of freshwater green microalgae. Our approach is based on a specialized level set method, leading to efficient and highly accurate algae segmentation. The level set formulation of our problem allows us to generate an algae's boundary curve as the result of an evolving level curve, based on computed background and algae regions in a given image...
This paper presents a multiphase image segmentation model based on Fuzzy Region Competition. The proposed model approximates image regions by probability density functions and uses a supervised approach in the segmentation process. The strategy of the proposed model is to perform two-phase Fuzzy Region Competition model several times, where a hard partition is obtained in each round from the determined...
In this paper, we propose an unsupervised variational two-phase image segmentation model based on Fuzzy Region Competition. This model uses probability density functions to design image regions and to set a homogeneity criterion for the competition between regions. The key idea of the proposed model is to optimize the probability distribution parameters while the segmentation procedure takes place...
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