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.
Domaines
Traitement du signal et de l'image [eess.SP] Automatique / Robotique Calcul parallèle, distribué et partagé [cs.DC] Arithmétique des ordinateurs Architectures Matérielles [cs.AR] Algorithme et structure de données [cs.DS] Vision par ordinateur et reconnaissance de formes [cs.CV] Traitement du signal et de l'image [eess.SP] Traitement des images [eess.IV] Robotique [cs.RO] Mathématique discrète [cs.DM] Génie logiciel [cs.SE]| Origine | Fichiers produits par l'(les) auteur(s) |
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