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- W3195758236 abstract "Superpixels intuitively over-segment an image into small partitions with homogeneity. Owing to the superiority of region-level description, it has been widely used in various computer vision applications as a substitute tool for pixels. However, there is still a disharmony between color homogeneity and shape regularity among existing superpixel algorithms, which hinders the performance of the task at hand. This paper introduces a novel Contour Optimized Non-Iterative Clustering (CONIC) superpixel segmentation method. It incorporates contour prior into the non-iterative clustering framework, thus providing a balanced trade-off between segmentation accuracy and visual uniformity. During the joint online assignment and updating step in the conventional Simple Non-Iterative Clustering (SNIC), a subtle feature distance is well-designed to measure the color similarity that considers contour constraint and prevents the boundary pixels from being assigned prematurely. Consequently, superpixels could acquire better visual quality and their boundaries are more consistent with the outlines of objects. Experiments on the Berkeley Segmentation Data Set 500 (BSDS500) verify that CONIC outperforms several state-of-the-art superpixel segmentation algorithms, in terms of both time efficiency and segmentation effects." @default.
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- W3195758236 date "2021-01-01" @default.
- W3195758236 modified "2023-09-23" @default.
- W3195758236 title "Superpixel Segmentation via Contour Optimized Non-Iterative Clustering" @default.
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- W3195758236 doi "https://doi.org/10.1007/978-981-16-5188-5_46" @default.
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