Fast connected-component labeling
Pattern Recognition
A Run-Based One-Scan Labeling Algorithm
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
An efficient first-scan method for label-equivalence-based labeling algorithms
Pattern Recognition Letters
Light speed labeling for Risc architectures
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Parallel graph component labelling with GPUs and CUDA
Parallel Computing
Research note: Connected component labeling on a 2D grid using CUDA
Journal of Parallel and Distributed Computing
Light speed labeling: efficient connected component labeling on RISC architectures
Journal of Real-Time Image Processing
Detecting atmospheric rivers in large climate datasets
Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities
Experiments on union-find algorithms for the disjoint-set data structure
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
A comparative review of two-pass connected component labeling algorithms
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Hi-index | 0.00 |
We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (Theor Comput Sci 154(2):165–181, 1996). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster than the contour tracing program distributed by the original authors.