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Communication Dans Un Congrès Année : 2019

SparseCCL: Connected Components Labeling and Analysis for sparse images

Résumé

Connected components labeling and analysis for dense images have been extensively studied on a wide range of architectures. Some applications, like particles detectors in High Energy Physics, need to analyse many small and sparse images at high throughput. Because they process all pixels of the image, classic algorithms for dense images are inefficient on sparse data. We address this inefficiency by introducing a new algorithm specifically designed for sparse images. We show that we can further improve this sparse algorithm by specializing it for the data input format, avoiding a decoding step and processing multiple pixels at once. A benchmark on Intel and AMD CPUs shows that the algorithm is from ×1.6 to ×2.5 faster on sparse images.
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Dates et versions

hal-02343598 , version 1 (02-11-2019)

Identifiants

  • HAL Id : hal-02343598 , version 1

Citer

Arthur Hennequin, Ben Couturier, Vladimir Gligorov, Lionel Lacassagne. SparseCCL: Connected Components Labeling and Analysis for sparse images. DASIP 2019 - The Conference on Design and Architectures for Signal and Image Processing, Oct 2019, Montréal, Canada. ⟨hal-02343598⟩
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