The connected-component labeling problem: A review of state-of-the-art algorithms

PATTERN RECOGNITION

卷:70

Review

He, Lifeng^1,2  Ren, Xiwei¹  Gao, Qihang¹  Zhao, Xiao¹  Yao, Bin¹  Chao, Yuyan³ 

[1] Shaanxi Univ Sci & Technol, Artificial Intelligence Inst, Coll Elect & Informat Engn, Xian 710021, Shaanxi, Peoples R China

[2] Aichi Prefectural Univ, Fac Informat Sci & Technol, Nagakute, Aichi 4801198, Japan

[3] Nagoya Sangyo Univ, Fac Environm Informat & Business, Owariasahi, Aichi 4888711, Japan

关键词: Connected-component labeling;    Shape feature;    Image analysis;    Image understanding;    Pattern recognition;    Computer vision;   

DOI  :  10.1016/j.patcog.2017.04.018

来源: Elsevier

【 摘 要 】

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. (C) 2017 The Authors. Published by Elsevier Ltd.

【 授权许可】

Free