The connected-component labeling problem: A review of state-of-the-art algorithms. (October 2017)

• Record Type: Journal Article
• Authors: He, Lifeng; Ren, Xiwei; Gao, Qihang; Zhao, Xiao; Yao, Bin; Chao, Yuyan
• Is Part Of: Pattern recognition. Volume 70(2017:Oct.)
• Page Start: 25
• Page End: 43
• Publication Date: 2017-10
• Subjects: Connected-component labeling -- Shape feature -- Image analysis -- Image understanding -- Pattern recognition -- Computer vision; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.04.018
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Connected-component labeling (CCL) is indispensable for pattern recognition. Many connected-component labeling algorithms have been proposed. The state-of-the-art CCL algorithms presented in the last decade are reviewed. Abstract: This article addresses the connected-component labeling problem which consists in assigning a unique label to all pixels of each connected component (i.e., each object) in a binary image. Connected-component labeling is indispensable for distinguishing different objects in a binary image, and prerequisite for image analysis and object recognition in the image. Therefore, connected-component labeling is one of the most important processes for image analysis, image understanding, pattern recognition, and computer vision. In this article, we review state-of-the-art connected-component labeling algorithms presented in the last decade, explain the main strategies and algorithms, present their pseudo codes, and give experimental results in order to bring order of the algorithms. Moreover, we will also discuss parallel implementation and hardware implementation of connected-component labeling algorithms, extension for n -D images, and try to indicate future work on the connected component labeling problem.