Block-based connected components labeling is by far the fastest algorithm to label the connected components in\r\n2D binary images, especially when the image size is quite large. This algorithm produces a decision tree that\r\ncontains 211 leaf nodes with 14 levels for the depth of a tree and an average depth of 1.5923. This article attempts\r\nto provide a faster method for connected components labeling. We propose two new scan masks for connected\r\ncomponents labeling, namely, the pixel-based scan mask and the block-based scan mask. In the final stage, the\r\nblock-based scan mask is transformed to a near-optimal decision tree. We conducted comparative experiments\r\nusing different sources of images for examining the performance of the proposed method against the existing\r\nmethods. We also performed an average tree depth analysis and tree balance analysis to consolidate the\r\nperformance improvement over the existing methods. Most significantly, the proposed method produces a\r\ndecision tree containing 86 leaf nodes with 12 levels for the depth of a tree and an average depth of 1.4593,\r\nresulting in faster execution time, especially when the foreground density is equal to or greater than the\r\nbackground density of the images.
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