Taming Voting Algorithms on Gpus for an Efficient Connected Component Analysis Algorithm
Résumé
Connected Component Analysis is vastly used as a building block for many Computer Vision algorithms from many fields like medical image processing, surveillance, or autonomous driving. It extends Connected Component Labeling by computing some features of the connected components like their bounding box or their surface. As such, Connected Component Analysis is a voting algorithm just like histogram computation or Hough transform. Voting algorithms are difficult on many-core architectures like GPUs because of the serialization of atomic memory accesses. The trend to increase the number of cores makes this issue even more critical. This paper explores multiple ways to reduce those conflicts for voting algorithms and especially for Connected Component Analysis. We show that our new algorithm is from 4 up to 10 times faster than State-of-the-Art on average on an Nvidia A100.
Domaines
Traitement du signal et de l'image [eess.SP] Automatique / Robotique Mathématique discrète [cs.DM] Génie logiciel [cs.SE] 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]| Origine | Fichiers produits par l'(les) auteur(s) |
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