2011
DOI: 10.1016/j.jpdc.2010.10.012
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Connected component labeling on a 2D grid using CUDA

Abstract: Connected component labeling is an important but computationally expensive operation required in many fields of research. The goal in the present work is to label connected components on a 2D binary map. Two different iterative algorithms for doing this task are presented. The first algorithm (Row-Col Unify) is based upon the directional propagation labeling, whereas the second algorithm uses the label equivalence technique. Row-Col Unify algorithm uses a local array of references and the reduction technique i… Show more

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Cited by 88 publications
(71 citation statements)
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“…One would expect a single-pass algorithm to be the most efficient; however, due to the non-sequential memory accesses required by the single-pass algorithm, the two-pass algorithms remain very competitive and execution-time scales linearly with the number of data elements (Wu et al 2009). Other areas of research into algorithm efficiency include identifying efficient data structures to store and attach labels to data elements such as the 'union-find' data structure (Fiorio & Gustedt 1996) and research into the parallelization of various connected-component algorithms including the use of Graphics Processing Units (GPUs) using NVIDIA's CUDA (Kalentev et al 2011).…”
Section: Connected-component Labellingmentioning
confidence: 99%
“…One would expect a single-pass algorithm to be the most efficient; however, due to the non-sequential memory accesses required by the single-pass algorithm, the two-pass algorithms remain very competitive and execution-time scales linearly with the number of data elements (Wu et al 2009). Other areas of research into algorithm efficiency include identifying efficient data structures to store and attach labels to data elements such as the 'union-find' data structure (Fiorio & Gustedt 1996) and research into the parallelization of various connected-component algorithms including the use of Graphics Processing Units (GPUs) using NVIDIA's CUDA (Kalentev et al 2011).…”
Section: Connected-component Labellingmentioning
confidence: 99%
“…The cluster algorithms were from Ref. [44] and the random number generator, a linear feedback shift register based on the XOR operator, from Ref. [45].…”
Section: Introductionmentioning
confidence: 99%
“…Typically, there were two propagation methods implemented in the CCL kernel, including directional pass and multi-way pass. Using CUDA, Kalentev et al [36] and Hashmi et al [37] implemented iterative row-column scanning on 2D binary grids. After initializing all non-zero element labels with their corresponding index, this algorithm propagated the lowest label value along the rows and the columns to label all non-zero neighbors with this lowest value.…”
Section: Related Workmentioning
confidence: 99%