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Labeling Color 2D Digital Images in Theoretical Near Logarithmic Time

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10425))

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Abstract

A design of a parallel algorithm for labeling color flat zones (precisely, 4-connected components) of a gray-level or color 2D digital image is given. The technique is based in the construction of a particular Homological Spanning Forest (HSF) structure for encoding topological information of any image. HSF is a pair of rooted trees connecting the image elements at inter-pixel level without redundancy. In order to achieve a correct color zone labeling, our proposal here is to correctly building a sub-HSF structure for each image connected component, modifying an initial HSF of the whole image. For validating the correctness of our algorithm, an implementation in OCTAVE/MATLAB is written and its results are checked. Several kinds of images are tested to compute the number of iterations in which the theoretical computing time differs from the logarithm of the width plus the height of an image. Finally, real images are to be computed faster than random images using our approach.

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References

  1. Abubaker, A., Qahwaji, R., Ipson, S., Saleh, M.: One scan connected component labeling technique. In: IEEE International Conference on Signal Processing and Communications, pp. 1283–1286. IEEE (2007)

    Google Scholar 

  2. Alnuweiri, H.M., Prasanna, V.K.: Parallel architectures and algorithms for image component labeling. IEEE T. Pattern Anal. 10, 1014–1034 (1992)

    Article  Google Scholar 

  3. Ballard, D.H., Brown, C.M.: Computer Vision. Prentice-Hall, Upper Saddle River (1982)

    Google Scholar 

  4. Braquelaire, J.P., Brun, L.: Image segmentation with topological maps and inter-pixel representation. J. Vis. Commun. Image R. 9(1), 62–79 (1998)

    Article  Google Scholar 

  5. Chang, F., Chen, C.J., Lu, C.J.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Und. 93(2), 206–220 (2004)

    Article  Google Scholar 

  6. Crespo, J., Schafer, R.W.: The flat zone approach and color images. In: Serra, J., Soille, P. (eds.) Mathematical Morphology and Its Applications to Image Processing Computational Imaging and Vision, vol. 2, pp. 85–92. Springer, Dordrecht (1994)

    Chapter  Google Scholar 

  7. Diaz-del-Rio, F., Real, P., Onchis, D.M.: A parallel homological spanning forest framework for 2D topological image analysis. Pattern Recogn. Lett. 83, 49–58 (2016)

    Article  Google Scholar 

  8. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Internat. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  9. Grana, C., Borghesani, D., Cucchiara, R.: Optimized Block-based connected-component labeling with decision trees. IEEE T. Image Process. 19(6), 1596–1609 (2010)

    Article  MathSciNet  Google Scholar 

  10. Han, Y., Wagner, R.A.: An efficient and fast parallel-connected component algorithm. J. ACM 37(3), 626–642 (1990)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. He, L., Chao, Y., Yang, Y., Li, S., Zhao, X., Suzuki, K.: A novel two-scan connected-component labeling algorithm. In: Yang, G.-C., Ao, S.-L., Gelman, L. (eds.) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol. 229, pp. 445–459. Springer, Dordrecht (2013)

    Chapter  Google Scholar 

  13. Hesselink, H., Meijster, A., Bron, C.: Concurrent determination of connected components. Sci. Comp. Programm. 41, 173–194 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hu, Q., Qian, G., Nowinski, W.L.: Fast connected-component labeling in three-dimensional binary images based on iterative recursion. Comput. Vis. Image Und. 99(3), 414–434 (2005)

    Article  Google Scholar 

  15. Johnston, C.T., Bailey, D.G.: FPGA implementation of a single pass connected components algorithm. In: 4th IEEE International Symposium on Electronic Design, Test and Applications, pp. 228–231. IEEE(2008)

    Google Scholar 

  16. Kalentev, O., Rai, A., Kemnitz, S., Schneider, R.: Connected component labeling on a 2D grid using CUDA. J. Parallel Distrib. Comput. 71(4), 615–620 (2011)

    Article  Google Scholar 

  17. Kropatsch, W.G.: Building irregular pyramids by dual-graph contraction. IEEE Proc. Vis. Image Signal Process. 142(6), 366–374 (1995)

    Article  Google Scholar 

  18. Kovalevsky, V.: Geometry of Locally Finite Spaces. Publishing House Dr. Baerbel Kovalevski, Berlin (2008)

    Google Scholar 

  19. Meyer, F.: From connected operators to leveling. In: Mathematical Morphology and its Applications to Image and Signal Processing. Computational Imaging and Vision, vol. 12, pp. 191–198. Kluwer Academic Publishers (1998)

    Google Scholar 

  20. Molina-Abril, H., Real, P.: Homological spanning forest framework for 2D image analysis. Annals Math. Artificial Intell. 4(64), 385–409 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Montanvert, A., Meer, P., Rosenfeld, A.: Hierarchical image analysis using irregular tessellations. IEEE Trans. Pattern Anal. Mach. Intell. 13(4), 307–316 (1991)

    Article  Google Scholar 

  22. Mandler, E., Oberlander, M.F.: One-pass encoding of connected components in multi-valued images. In: Proceedings of the IEEE International Conference on Pattern Recognition, vol. 2, pp. 65–69 (1990)

    Google Scholar 

  23. Niknam, M., Thulasiraman, P., Camorlinga, S.A.: A parallel algorithm for connected component labeling of gray-scale images on homogeneous multicore architectures. J. Phys: Conf. Ser. 256(012010), 1–7 (2010)

    Google Scholar 

  24. Rosenfeld, A., Pfaltz, J.L.: Sequential operations in digital picture processing. J. ACM 13(4), 471–494 (1966)

    Article  MATH  Google Scholar 

  25. Samet, H.: Connected-component labeling using quadtrees. J. ACM 28(3), 487–501 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  26. Schwenk, K., Huber, F.: Connected-component labeling algorithm for very complex and high-resolution images on an FPGA platform. In: SPIE Remote Sensing, ISOP (2015)

    Google Scholar 

  27. 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 the 10th International Conference Pattern Recognition, pp. 655–658, June 1990

    Google Scholar 

  28. Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Comput. Vis. Image Und. 89(1), 1–23 (2003)

    Article  MATH  Google Scholar 

  29. Sang, H., Zhang, J., Zhang, T.: Efficient multi-value connected component labeling algorithm and its ASIC design. In: Proceedings of the SPIE Medical Imaging Conference (2007). 67892I

    Google Scholar 

  30. Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Anal. Appl. 12(2), 117–135 (2009)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work has been supported by the Spanish research projects (AEI/FEDER,UE) TEC2016-77785-P and MTM2016-81030-P. The last co-author gratefully acknowledges the support of the Austrian Science Fund FWF-P27516.

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Correspondence to P. Real .

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Díaz-del-Río, F., Real, P., Onchis, D. (2017). Labeling Color 2D Digital Images in Theoretical Near Logarithmic Time. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_33

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  • DOI: https://doi.org/10.1007/978-3-319-64698-5_33

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