An efficient first-scan method for label-equivalence-based labeling algorithms

Abstract

Label-equivalence-based connected-component labeling algorithms complete labeling in two or more raster scans. In the first scan, each foreground pixel is assigned a provisional label, and label equivalences between provisional labels are recorded. For doing this task, all conventional algorithms use the same mask that consists of four processed neighbor pixels to process every foreground pixel. This paper presents a simple yet efficient first-scan method for label-equivalence-based labeling algorithms. In our method, foreground pixels following a background pixel and those following a foreground pixel are processed in a different way. By use of this idea, the pixel followed by the current foreground pixel can be removed from the mask. In other words, the mask used in our method consists of three processed neighbor pixels. Thus, for processing a foreground pixel, the average number of times for checking the processed neighbor pixels in the first scan is reduced from 2.25 to 1.75. Because the current foreground pixel following a background pixel or a foreground pixel can be known without any additional computing cost, our method is efficient for any image that contains at least one foreground pixel. Experimental results demonstrated that our method is effective for improving the efficiency of label-equivalence-based labeling algorithms.