Connected component labeling of binary images on a mesh connected massively parallel processor
Computer Vision, Graphics, and Image Processing
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Digital Picture Processing
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Optimizing two-pass connected-component labeling algorithms
Pattern Analysis & Applications
Parallel graph component labelling with GPUs and CUDA
Parallel Computing
Finding extremal sets on the GPU
Journal of Parallel and Distributed Computing
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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. The Row-Col Unify algorithm uses a local array of references and the reduction technique intrinsically. The usage of shared memory extensively makes the code efficient. The Label Equivalence algorithm is an extended version of the one presented by Hawick et al. (2010) [3]. At the end the comparison depending on the performances of both of the algorithms is presented.