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Firstly, the image is adaptively enhanced by simple linear iterative clustering (SLIC) combined with isotropic nonlinear filtering (INF).
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In the current study, the superpixels are generated by the simple linear iterative clustering (SLIC) method [12] because they process with limited computational effort and the superpixels adhere to the boundaries well.
We overly segment the recent n consecutive frames {F t } into superpixels by using a modification of a state-of-the-art algorithm termed simple linear iterative clustering (SLIC) [29].
b Results of simple linear iterative clustering.
Simple linear iterative clustering is an adaptation of K-means for superpixel generation.
Simple linear iterative clustering (SLIC) super-voxels segment each nucleus into a small number of regions while usually respecting the boundaries between different objects (Fig. 3C).
Algorithms such as Simple Linear Iterative Clustering (SLIC) [ 195] have been developed to aggregate nearby pixels into superpixels whose boundaries closely match true image boundaries.
Factors included in multiple linear regression analysis were selected among variables yielding P < 0.1 by simple linear regression analysis.
Relationships between continuous variables were assessed by simple linear regression analysis.
Scatter plots were evaluated by simple linear regression analysis.
Relationships between continuous variables were examined by simple linear regression.
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