Abstract
This chapter proposes a novel two-scan labeling algorithm. In the first scan, all conventional two-scan labeling algorithms process image lines one by one, assigning each foreground pixel a provisional label, finding and resolving label equivalences between provisional labels. In comparison, our proposed method first scans image lines every four lines, assigns provisional labels to foreground pixels among each three lines, and finds and resolves label equivalences among those provisional labels. Then, it processes the leaving lines from top to bottom one by one, and for each line, assigns provisional labels to foreground pixels on the line, and finds and resolves label equivalences among the provisional labels and those assigned to the foreground pixels on the lines immediately above and below the current line. With our method, the average number of times for checking pixels for processing a foreground pixel will decrease; thus, the efficiency of labeling can be improved. Experimental results demonstrated that our method was more efficient than conventional label-equivalence-based labeling algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexey A, Tomas K, Florentin W, Babette D (2011) Real-time image segmentation on a GPU. Facing Multicore Chall Lect Notes Comput Sci 6310:131–142
Ballard DH (1982) Computer vision. Prentice-Hall, Englewood
Chang F, Chen CJ, Lu CJ (2004) A linear-time component-labeling algorithm using contour tracing technique. Comput Vis Image Underst 93:206–220
Christopher W, Nicholas Graham TC, Pape JA (2010) Seeing through the fog: an algorithm for fast and accurate touch detection in optical tabletop surfaces. In: ACM international conference on interactive tabletops and surfaces (ITS ’10). ACM, New York, USA, pp 73–82 (2010)
Dellen B, Erdal EA, Wrgtter F (2009) Segment tracking via a spatiotemporal linking process including feedback stabilization in an n-D lattice model. Sensors 9(11):9355–9379
Gonzalez RC, Woods RE (1992) Digital image processing. Addison Wesley, Reading
Haralick RM (1981) Some neighborhood operations. In: Real time/parallel computing image analysis. Plenum Press, New York, pp 11–35
Haralick RM, Shapiro LG (1992) Computer and robot vision I. Addison-Wesley, Reading, pp 28–48
Hashizume A, Suzuki R, Yokouchi H et al (1990) An algorithm of automated RBC classification and its evaluation. Bio Med Eng 28(1):25–32
He L, Chao Y, Suzuki K (2007) A linear-time two-scan labeling algorithm. In: 2007 IEEE international conference on image processing (ICIP). San Antonio, Texas, USA, pp V-241-V-244
He L, Chao Y, Suzuki K (2008) A run-based two-scan labeling algorithm. IEEE Trans Image Process 17(5):749–756
He L, Chao Y, Suzuki K, Wu K (2009) Fast connected-component labeling. Pattern Recognit 42:1977–1987
He L, Chao Y, Suzuki K (2010) An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognit Lett 31:28–35
He L, Chao Y, Suzuki K (2011) A run-based one-and-a-half-scan connected-component labeling algorithm. Int J Pattern Recognit Artif Intell 24(4):557–579
He L, Chao Y, Suzuki K (2011) Two efficient label-equivalence-based connected-component labeling algorithms for three-dimensional binary images. IEEE Trans Image Process 20(8):2122–2134
He L, Chao Y, Suzuki K (2012) A new two-scan algorithm for labeling connected components in binary images. Lecture notes in engineering and computer science: proceedings of the world congress on engineering 2012, WCE, 4–6 July 2012 U.K , London, pp 1141–1146
Hu Q, Qian G, Nowinski WL (2005) Fast connected-component labeling in three-dimensional binary images based on iterative recursion. Comput Vis Image Underst 99:414–434
Lumia R, Shapiro L, Zungia O (1983) A new connected components algorithm for virtual memory computers. Comput Vis Graph Image Process 22(2):287–300
Naoi S (1995) High-speed labeling method using adaptive variable window size for character shape feature. IEEE Asian Conf Comput Vis 1:408–411
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66
Ronsen C, Denjiver PA (1984) Connected components in binary images: the detection problem. Research Studies Press, New York
Rosenfeld A, Pfalts JL (1966) Sequential operations in digital picture processing. J ACM 13(4):471–494
Samet H (1984) The quadtree and related hierarchical data structures. Comput Surv 16(2):187–260
Shima Y, Murakami T, Koga M, Yashiro H, Fujisawa H (1990) A high-speed algorithm for propagation-type labeling based on block sorting of runs in binary images. In: Proceedings of 10th international conference pattern recognition, pp 655–658
Suzuki K, Horiba I, Sugie N (2003) Linear-time connected-component labeling based on sequential local operations. Comput Vis Image Underst 89:1–23
Udupa JK, Ajjanagadde VG (1990) Boundary and object labeling in three-dimensional images. Comput Vis Graph Image Process 51(3):355–369
Acknowledgments
This work was supported in part by the Ministry of Education, Science, Sports and Culture, Japan, Grant-in-Aid for Scientific Research (C), 23500222, 2011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
He, L., Chao, Y., Yang, Y., Li, S., Zhao, X., Suzuki, K. (2013). A Novel Two-Scan Connected-Component Labeling Algorithm. In: Yang, GC., Ao, Sl., Gelman, L. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6190-2_34
Download citation
DOI: https://doi.org/10.1007/978-94-007-6190-2_34
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-6189-6
Online ISBN: 978-94-007-6190-2
eBook Packages: EngineeringEngineering (R0)