Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary and object labelling in three-dimensional images
Computer Vision, Graphics, and Image Processing
CVGIP: Graphical Models and Image Processing
Connected component labeling for binary images on a reconfigurable mesh architecture
Journal of Systems Architecture: the EUROMICRO Journal
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
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
A run-based two-scan labeling algorithm
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Connected components labeling for giga-cell multi-categorical rasters
Computers & Geosciences
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We propose two new methods to label connected components based on iterative recursion: one directly labels an original binary image while the other labels the boundary voxels followed by one-pass labelling of non-boundary object voxels. The novelty of the proposed methods is a fast labelling of large datasets without stack overflow and a flexible trade-off between speed and memory. For each iterative recursion: (1) the original volume is scanned in the raster order and an initially unlabelled object voxel v is selected, (2) a sub-volume with a user-defined size is formed around the selected voxel v, (3) within this sub-volume all object voxels 26-connected to v are labelled using iterations; and (4) subsequent iterative recursions are initiated from those border object voxels of the sub-volume that are 26-connected to v. Our experiments show that the time-memory trade-off is that the decrease in the execution time by one-third requires the increase in memory size by 3 orders. This trade-off is controlled by the user by changing the size of the sub-volume. Experiments on large three-dimensional brain phantom datasets (362x432x362 voxels of 56 MB (megabytes)) show that the proposed methods are three times faster on the average (with the maximum speedup of 10) than the existing iterative methods based on label equivalences with less than 1 MB memory consumption. Moreover, our algorithms are applicable to any dimensional data and are less dependant on the geometric complexity of connected components.