A gamma-signal-regulated connected components labeling algorithm. (July 2019)

• Record Type: Journal Article
• Authors: Zhang, Danyang; Ma, Huadong; Pan, Linqiang
• Is Part Of: Pattern recognition. Volume 91(2019:Jul.)
• Page Start: 281
• Page End: 290
• Publication Date: 2019-07
• Subjects: Connected components labeling -- Object detection -- Object recognition -- Pattern recognition -- Image analysis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.02.022
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper proposes a novel connected components labeling (CCL) approach, a gamma-signal-regulated approach, for higher efficiency. A new and more efficient block-based two-scan CCL algorithm, Eight-Connected Gamma-Signal-regulated (ECGS) algorithm, is proposed. Experiment results have demonstrated that ECGS can outperform current state-of-the-art CCL algorithms for a number of digital images. Abstract: This paper proposes a novel connected components labeling (CCL) approach that introduces a gamma signal to record certain mask pixels' values to eliminate duplicated pixel checking and regulate the labeling process for higher efficiency. A new block-based two-scan CCL algorithm, Eight-Connected Gamma-Signal-regulated (ECGS) algorithm, is designed and developed by applying this approach to evaluate a block of 2 × 2 pixels (with just 6 mask pixels) in each iteration such that the total number of operations is considerably reduced and the labeling efficiency is significantly improved. The experiments conducted on a public benchmark, YACCLAB (Yet Another Connected Components Labeling Benchmark), have demonstrated that the proposed ECGS algorithm can outperform current state-of-the-art CCL algorithms for a number of digital images.