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- W3095915165 abstract "This paper introduces a novel method for segmentation of clustered partially overlapping convex objects in silhouette images. The proposed method involves three main steps: pre-processing, contour evidence extraction, and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image by detecting concave points. After this the contour segments which belong to the same objects are grouped. The grouping is formulated as a combinatorial optimization problem and solved using the branch and bound algorithm. Finally, the full contours of the objects are estimated by a Gaussian process regression method. The experiments on a challenging dataset consisting of nanoparticles demonstrate that the proposed method outperforms three current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have a convex shape." @default.
- W3095915165 created "2020-11-09" @default.
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- W3095915165 date "2020-11-01" @default.
- W3095915165 modified "2023-10-07" @default.
- W3095915165 title "Resolving overlapping convex objects in silhouette images by concavity analysis and Gaussian process" @default.
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- W3095915165 doi "https://doi.org/10.1016/j.jvcir.2020.102962" @default.
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