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A Run-Based One-Scan Labeling Algorithm

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Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

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Abstract

This paper presents a run-based one-scan algorithm for labeling connected components in a binary image. Our algorithm is different with conventional raster-scan label-equivalence-based algorithms in two ways: (1) to complete connected component labeling, all conventional label-equivalence-based algorithms scan a whole image two or more times, our algorithm scans a whole image only once; (2) all conventional label-equivalence-based algorithms assign each object pixel a provisional label in the first scan and rewrite it in later scans, our algorithm does not assign object pixels but runs provisional labels. In the scan, our algorithm records all run data in an image in a one-dimensional array and assigns a provisional label to each run. Any label equivalence between runs is resolved whenever it is found in the scan, where the smallest label is used as their representative label. After the scan finished, all runs that belong to a connected component will hold the same representative label. Then, using the recorded run data, each object pixel of a run is assigned the representative label corresponding to the run without rewriting the values (i.e., provisional labels) of object pixels and scanning any background pixel again. Experimental results demonstrate that our algorithm is extremely efficient on images with long runs or small number of object pixels.

This work was partially supported by the TOYOAKI Scholarship Foundation, Japan.

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References

  1. Ballard, D.H.: Computer Vision. Prentice-Hall, Englewood, New Jesey (1982)

    Google Scholar 

  2. Chang, F., Chen, C.J., Lu, C.J.: A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding 93, 206–220 (2004)

    Article  Google Scholar 

  3. Regentova, E., Latifi, S., Deng, S., Yao, D.: An Algorithm with Reduced Operations for Connected Components Detection in ITU-T Group 3/4 Coded Images. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1039–1047 (2002)

    Article  Google Scholar 

  4. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  5. Gotoh, T., Ohta, Y., Yoshida, M., Shirai, Y.: Component labeling algorithm for video rate processing. In: Proc. SPIE. Advances in Image Processing, April 1987, vol. 804, pp. 217–224 (1987)

    Google Scholar 

  6. Haralick, R.M.: Some neighborhood operations. In: Real Time/Parallel Computing Image Analysis, pp. 11–35. Plenum Press, New York (1981)

    Google Scholar 

  7. Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. I, pp. 28–48. Addison-Wesley, Reading (1992)

    Google Scholar 

  8. Hashizume, A., Suzuki, R., Yokouchi, H., et al.: An algorithm of automated RBC classification and its evaluation. Bio Medical Engineering 28(1), 25–32 (1990)

    Google Scholar 

  9. He, L., Chao, Y., Suzuki, K., Wu, K.: Fast Connected-Component Labeling. Pattern Recognition, doi:10.1016/j.patcog.2008.10.013

    Google Scholar 

  10. He, L., Chao, Y., Suzuki, K.: A Run-based Two-Scan Labeling Algorithm. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 131–142. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. He, L., Chao, Y., Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Transactions on Image Processing 17(5), 749–756 (2008)

    Article  MathSciNet  Google Scholar 

  12. Hu, Q., Qian, G., Nowinski, W.L.: Fast connected-component labeling in three-dimensional binary images based on iterative recursion. Computer Vision and Image Understanding 99, 414–434 (2005)

    Article  Google Scholar 

  13. Kim, S.D., Lee, J.H., Kim, J.K.: A new chain-coding algorithm for binary images using run-length codes. Computer Vision, Graphics, and Image Processing 41(1), 114–128 (1988)

    Article  Google Scholar 

  14. Komeichi, M., Ohta, Y., Gotoh, T., Mima, T., Yoshida, M.: Video-rate labeling processor. In: Proc. SPIE. Image Processing II, September 1988, vol. 1027, pp. 69–76 (1988)

    Google Scholar 

  15. Lumia, R., Shapiro, L., Zungia, O.: A new connected components algorithm for virtual memory computers. Computer Vision, Graphics, and Image Processing 22(2), 287–300 (1983)

    Article  Google Scholar 

  16. Lumia, R.: A new three-dimensional connected components algorithm. Computer Vision, Graphics, and Image Processing 23(2), 207–217 (1983)

    Article  Google Scholar 

  17. Manohar, M., Ramapriyan, H.K.: Connected component labeling of binary images on a mesh connected massively parallel processor. Computer Vision, Graphics, and Image Processing 45(2), 133–149 (1989)

    Article  Google Scholar 

  18. Martin-Herrero, J.: Hybrid object labelling in digital images. Mach. Vision Appl. 18(1), 1–15 (2007)

    Article  Google Scholar 

  19. Nagy, G., Seth, S.C., Stoddard, S.D.: Document analysis with an expert system. In: Proc. ACM Conf. Document Processing Systems, pp. 169–176 (1988)

    Google Scholar 

  20. Naoi, S.: High-speed labeling method using adaptive variable window size for character shape feature. In: IEEE Asian Conf. Computer Vision, December 1995, vol. 1, pp. 408–411 (1995)

    Google Scholar 

  21. Nassimi, D., Sahani, S.: Finding connected components and connected ones on a mesh connected parallel compute. SIAM Jour. Computer 9(4), 744–757 (1980)

    Article  MATH  Google Scholar 

  22. Nicol, C.J.: A systolic approach for real time connected component labeling. Computer Vision and Image Understanding 61(1), 17–31 (1995)

    Article  Google Scholar 

  23. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Systems Man and Cybernetics 9, 62–66 (1979)

    Article  Google Scholar 

  24. Partridge, C.S.: Method of skeletonizing a binary image using compressed run length data, United States Patent 6058219 (May 2000), http://www.patentstorm.us/patents/6058219.html

  25. Ronsen, C., Denjiver, P.A.: Connected Components in Binary Images: The Detection Problem. Research Studies Press (1984)

    Google Scholar 

  26. Rosenfeld, A., Pfalts, J.L.: Sequential operations in digital picture processing. Journal of ACM 13(4), 471–494 (1966)

    Article  MATH  Google Scholar 

  27. Rosenfeld, A.: Connectivity in digital pictures. Journal of ACM 17(1), 146–160 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  28. Rosenfeld, A., Kak, A.C.: Digital Picture Processing, 2nd edn., vol. 2. Academic Press, San Diego (1982)

    MATH  Google Scholar 

  29. Shima, Y., Murakami, T., Koga, M., Yashiro, H., Fujisawa, H.: A high-speed algorithm for propagation-type labeling based on block sorting of runs in binary images. In: Proceedings of 10th International Conference on Pattern Recognition, pp. 655–658 (1990)

    Google Scholar 

  30. Shin, J., Hwang, H., Chien, S.: Detecting fingerprint minutiae by run length encoding scheme. Pattern recognition 39(6), 1140–1154 (2006)

    Article  MATH  Google Scholar 

  31. Shirai, Y.: Labeling connected regions. In: Three-Dimensional Computer Vision, pp. 86–89. Springer, Heidelberg (1987)

    Chapter  Google Scholar 

  32. Shiraishi, N.: Image data compression apparatus for compressing both binary image data and multiple. United States Patent 6941023 (September 2005), http://www.patentstorm.us/patents/6941023-claims.html

  33. Shoji, K., Miyamichi, J.: Connected Component Labeling in Binary Images by Run-Based Contour Tracing. The Transactions of the Institute of Electronics, Information and Communication Engineers D-II J83-D-II(4), 1131–1139 (in Japanese)

    Google Scholar 

  34. Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding 89, 1–23 (2003)

    Article  MATH  Google Scholar 

  35. Tsuiki, T., Aoki, T., Kino, S.: Image processing based on a runlength coding and its application to an intelligent facsimile. In: Proc. Conf. Record, GLOBECOM 1982, November 1982, pp. B6.5.1–B6.5.7 (1982)

    Google Scholar 

  36. Wang, K.B., Chia, T.L., Chen, Z.: Parallel execution of a connected component labeling operation on a linear array architecture. J. of Information Science and Engineering 19, 353–370 (2003)

    Google Scholar 

  37. Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Analysis & Applications, doi:10.1007/s10044-008-0109-y

    Google Scholar 

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Author information

Authors and Affiliations

  1. The Faculty of Electronical and Information Engineering Shannxi University of Science and Technology, Shannxi 710021, China The Graduate School of Information Science and Technology, Aichi Prefectural University, Aichi, 480-1198, Japan

    Lifeng He

  2. The Faculty of Mechanicial and Electronical Engineering Shannxi University of Science and Technology, Shannxi 710021, China The Graduate School of Environment Management, Nagoya Sangyo University, Owariasahi, Aichi, 488-8711, Japan

    Yuyan Chao

  3. The Department of Radiology, Division of the Biological Sciences, The University of Chicago, Chicago, IL, 60637, USA

    Kenji Suzuki

  4. The Department of Information Engeering, Nagoya Institute of Technology, Showa-ku, Nagoya, 466-8666, Japan

    Hidenori Itoh

Authors
  1. Lifeng He
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  2. Yuyan Chao
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  3. Kenji Suzuki
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  4. Hidenori Itoh
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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He, L., Chao, Y., Suzuki, K., Itoh, H. (2009). A Run-Based One-Scan Labeling Algorithm. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

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