Connected Components Labeling (CCL) is a fundamental image processing technique, widely used in various application areas. Computational throughput of Graphical Processing Units (GPUs) makes them eligible for such a kind of algorithms. In the last decade, many approaches to compute CCL on GPUs have been proposed. Unfortunately, most of them have focused on 4-way connectivity neglecting the importance of 8-way connectivity. This paper aims to extend state-of-the-art GPU-based algorithms from 4 to 8-way connectivity and to improve them with additional optimizations. Experimental results revealed the effectiveness of the proposed strategies.
Optimizing GPU-Based Connected Components Labeling Algorithms / Allegretti, Stefano; Bolelli, Federico; Cancilla, Michele; Grana, Costantino. - (2018), pp. 175-180. (Intervento presentato al convegno 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS) tenutosi a Inria Sophia Antipolis, France nel Dec 12-14) [10.1109/IPAS.2018.8708900].
Optimizing GPU-Based Connected Components Labeling Algorithms
ALLEGRETTI, STEFANO;Federico Bolelli
;Michele Cancilla;Costantino Grana
2018
Abstract
Connected Components Labeling (CCL) is a fundamental image processing technique, widely used in various application areas. Computational throughput of Graphical Processing Units (GPUs) makes them eligible for such a kind of algorithms. In the last decade, many approaches to compute CCL on GPUs have been proposed. Unfortunately, most of them have focused on 4-way connectivity neglecting the importance of 8-way connectivity. This paper aims to extend state-of-the-art GPU-based algorithms from 4 to 8-way connectivity and to improve them with additional optimizations. Experimental results revealed the effectiveness of the proposed strategies.File | Dimensione | Formato | |
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