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- W2605580863 abstract "We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel generation. This results in densely distributed, small, and precise supervoxels in salient regions which often contain objects, and larger supervoxels in less salient regions that often correspond to background. Our approach largely improves the quality of the resulting supervoxel segmentation in terms of boundary recall and under-segmentation error on publicly available benchmarks." @default.
- W2605580863 created "2017-04-28" @default.
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- W2605580863 date "2017-04-13" @default.
- W2605580863 modified "2023-10-17" @default.
- W2605580863 title "Saliency-guided Adaptive Seeding for Supervoxel Segmentation" @default.
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- W2605580863 doi "https://doi.org/10.48550/arxiv.1704.04054" @default.
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