A Run-Based One-and-a-Half-Scan Connected-Component Labeling Algorithm

This paper presents a runand label-equivalence-based one-and-a-half-scan algorithm for labeling connected components in a binary image. Major di®erences between our algorithm and conventional label-equivalence-based algorithms are: (1) all conventional label-equivalencebased algorithms scan all pixels in the given image at least twice, whereas our algorithm scans background pixels once and object pixels twice; (2) all conventional label-equivalence-based algorithms assign a provisional label to each object pixel in the ̄rst scan and relabel the pixel in the later scan(s), whereas our algorithm assigns a provisional label to each run in the ̄rst scan, and after resolving label equivalences between runs, by using the recorded run data, it assigns each object pixel a ̄nal label directly. That is, in our algorithm, relabeling of object pixels is not necessary any more. Experimental results demonstrated that our algorithm is highly e±cient on images with many long runs and/or a small number of object pixels. Moreover, our algorithm is directly applicable to run-length-encoded images, and we can obtain contours of connected components e±ciently.