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- W2885383822 abstract "Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading)." @default.
- W2885383822 created "2018-08-22" @default.
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- W2885383822 date "2018-07-23" @default.
- W2885383822 modified "2023-09-26" @default.
- W2885383822 title "Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach" @default.
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- W2885383822 doi "https://doi.org/10.54294/8hic7f" @default.
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