Optimized Block-Based Connected Components Labeling With Decision Trees

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based sc...

Connected components labeling - algorithms in

An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs

In this paper, we report on an optimized union-find (UF) algorithm that can label the connected components on a 2D image efficiently by employing GPU architecture. The proposed method comprises three phases: UF-based local merge, boundary analysis, and link. The coarse labeling in local merge, which makes computation efficient because the length of the labelequivalence list is sharply suppresse...

Connected components labeling for giga-cell multi-categorical rasters

Labeling of connected components in an image or a raster of non-imagery data is a fundamental operation in fields of pattern recognition and machine intelligence. The bulk of effort devoted to designing efficient connected components labeling (CCL) algorithms concentrated on the domain of binary images where labeling is required for a computer to recognize objects. In contrast, in the Geographi...

Efficient scan mask techniques for connected components labeling algorithm

Block-based connected components labeling is by far the fastest algorithm to label the connected components in 2D binary images, especially when the image size is quite large. This algorithm produces a decision tree that contains 211 leaf nodes with 14 levels for the depth of a tree and an average depth of 1.5923. This article attempts to provide a faster method for connected components labelin...