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Detection of Concave Points in Closed Object Boundaries Aiming at Separation of Overlapped Objects

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1378))

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Abstract

Separation of overlapping objects is one of the critical pre-processing tasks in biomedical and industrial applications. Separation of the attached objects is necessary, aiming at some qualitative research analysis or diagnosis of some existing behavior. It has been observed that a partial overlap between two or more convex shape objects introduces concave regions over the boundary. Here, in this paper, we present a novel approach for detecting the concavities along the overlapped object boundaries. The proposed algorithm works by computing the visibility matrix concerning the boundary pixels, which works as a variant of the chord method. Furthermore, our proposed method selects strong boundary candidates using the visibility matrix to ignore smaller concave-zones. Some distance thresholds introduced by us guide the selection. These detected concave points might be used subsequently for the separation and counting of objects. Experimental results show the degree of correctness and usefulness of the proposed method.

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References

  1. Angulo, J., Jeulin, D.: Stochastic watershed segmentation. In: Proceeding of the 8th International symposium on Mathematical Morphology, pp. 265–276 (2007)

    Google Scholar 

  2. Bai, X., Sun, C., Zhou, F.: Splitting touching cells based on concave points and ellipse fitting. Pattern Recognit. 42, 2434–2446 (2009)

    Article  Google Scholar 

  3. Bera, S., Biswas, A., Bhattacharya, B.B.: A fast and automated granulometric image analysis based on digital geometry. Fundamenta Informaticae 138(3), 321–338 (2015)

    Article  MathSciNet  Google Scholar 

  4. Biswas, A., Bhowmick, P., Bhattcharya, B.B.: Construction of isothetic coves of a digital object: a combinational approach. J. Vis. Commun. Image Represent. 21(4), 295–310 (2010)

    Article  Google Scholar 

  5. Cates, J.E., Whitaker, R.T., Jones, G.M.: Case study: an evaluation of user-assisted hierarchical watershed segmentation. Med. Image Anal. 9(6), 566–578 (2005)

    Article  Google Scholar 

  6. Cheng, J., Rajapakse, J.: Segmentation of of clustered nuclei with shape markers and marker function. IEEE Trans. Biomed. Eng. 56(3), 741–748 (2009)

    Article  Google Scholar 

  7. Cristoforitti, A., Faes, L., Centonze, M., Antolini, R., Nollo, G.: Isolation of the left atrial surface from cardiac multi-detector CT images based on marker controlled watershed segmentation. Med. Eng. Phys. 30(1), 48–58 (2008)

    Article  Google Scholar 

  8. Canny, J.A.: Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  9. Charles, J.J., Kuncheva, L.I., Wells, B., Lim, I.S.: Object segmentation with in microscope image of palynofacies. Comput. Geosci. 34(6), 688–698 (2008)

    Article  Google Scholar 

  10. Dai, J., Chen, X., Chu, N.: Research on the extraction and classification of the concave point from fiber image. In: IEEE 12th International Conference on Signal Processing (ICSP), pp. 709–712 (2014)

    Google Scholar 

  11. Do, C.J., Sun, D.W.: Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture. Meat Sci. 72(2), 294–302 (2006)

    Article  Google Scholar 

  12. Farhan, M., Yli-Harja, O., Niemisto, A.: A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search. Pattern Recognit. 46, 741–751 (2013)

    Article  Google Scholar 

  13. Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.): ECCV 2014, Part IV. LNCS, vol. 8692. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2

    Book  Google Scholar 

  14. Iwanowski, M.: Morphological boundary pixel classification. In: Proceedings of the International Conference on “Computer as a Tool’ EUROCON 2007. IEEE (2007)

    Google Scholar 

  15. Jung, C., Kim, C.: Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans. Biomed. Eng. 57(10), 2600–2604 (2010)

    Article  Google Scholar 

  16. Jalba, A.C., Wilkinson, M.H., Roerdink, J.B.: Automatic segmentation of diatom images for classification. Microsc. Res. Tech. 65(1–2), 72–85 (2004)

    Article  Google Scholar 

  17. Kothari, S., Chaudry, Q., Wang, M.: Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques. In: IEEE International Symposium on Biomedical Imaging, pp. 795–798 (2009)

    Google Scholar 

  18. Kumar, S., Ong, S.H., Ranganath, S., Ong, T.C., Chew, F.T.: A rule-based approach for robust clump splitting. Pattern Recognit. 39, 1088–1098 (2006)

    Article  Google Scholar 

  19. Karantzalos, K., Argialas, D.: Improving edge detection and watershed segmentation with an isotropic diffusion and morphological levelling. Int. J. Remote Sens. 27(24), 5427–5434 (2006)

    Article  Google Scholar 

  20. Leprettre, B., Martin, N.: Extraction of pertinent subsets from time-frequency representations for detection and recognition purpose. Signal Process. 82(2), 229–238 (2002)

    Article  Google Scholar 

  21. Malcolm, A.A., Leong, H.Y., Spowage, A.C., Shacklock, A.P.: Image segmentation and analysis for porosity measurement. J. Mater. Proces. Technol. 192, 391–396 (2007)

    Article  Google Scholar 

  22. Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)

    Article  Google Scholar 

  23. Park, C., Huang, J.Z., Ji, J.X., Ding, Y.: Segmentation, inference and classification of partially overlapping nanoparticles. IEEE Trans. Pattern Anal. Mach. Intell 35(3), 669–681 (2013)

    Google Scholar 

  24. Park, S.C., Lim, S.H., Sin, B.K., Lee, S.W.: Tracking non-rigid objects using probabilistic Hausdorff distance matching. Pattern Recognit. 38(12), 2373–2384 (2005)

    Article  Google Scholar 

  25. Pratikakis, I.E., Sahli, H., Cornelis, J.: Low level Image partitioning guided by the gradient watershed hierarchy. Signal Process. 75(2), 173–195 (1999)

    Article  Google Scholar 

  26. Razdan, A., Bae, M.: A hybrid approach to feature segmentation of triangle meshes. Comput. Aided Des. 35(9), 783–789 (2003)

    Article  Google Scholar 

  27. Rosenfeld, A.: Measuring the sizes of concavities. Pattern Recognit. Lett. 3, 71–75 (1985)

    Article  Google Scholar 

  28. Samma, A.S.B., Talib, A.Z., Salam, R.A.: Combining boundary and skeleton information for convex and concave points detection. In: IEEE Seventh International Conference on Computer Graphics, Imaging and Visualization (CGIV), pp. 113–117 (2010)

    Google Scholar 

  29. Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recognit 33, 225–236 (2000)

    Article  Google Scholar 

  30. Shu, J., Fu, H., Qiu, G., Kaye, P., IIyas, M.: Segmenting overlapping cell nuclei in digital histopathology images. In: 35th International Conference on Medicine and Biology Society (EMBC), pp. 5445–5448 (2013)

    Google Scholar 

  31. Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., Wang, T.: Accurate segmentation of cervical cytoplasm and nuclei based on multi scale convolutional neural network and graph partitioning. IEEE Trans. Biomed. Eng. 62(10), 2421–2433 (2015)

    Article  Google Scholar 

  32. Vincent, L.: Fast granulometric methods for the extraction of global image information. In: Proceedings, 11 Annual Symposium of the South African Pattern Recognition Association

    Google Scholar 

  33. Wang, D.: Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Trans. Circuits Syst. Video Technol. 8(5), 539–546 (1998)

    Article  Google Scholar 

  34. Wen, Q., Chang, H., Parvin, B.: A delaunay triangulation approach for segmenting clumps of nuclei. In: Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro, pp. 9–12, Boston, USA, 2009

    Google Scholar 

  35. Zafari, S., Eerola, T., Sampo, J., Kälviäinen, H., Haario, H.: Segmentation of partially overlapping nanoparticles using concave points. In: Bebis, G., et al. (eds.) ISVC 2015, Part I. LNCS, vol. 9474, pp. 187–197. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27857-5_17

    Chapter  MATH  Google Scholar 

  36. Zhang, W.H., Jiang, X., Liu, Y.M.: A method for recognizing overlapping elliptical bubbles in bubble image. Pattern Recognit. Lett. 33, 1543–1548 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Information Technology, Kalyani, India

    Sourav Chandra Mandal, Oishila Bandyopadhyay & Sanjoy Pratihar

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  1. Sourav Chandra Mandal
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  2. Oishila Bandyopadhyay
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  3. Sanjoy Pratihar
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Corresponding authors

Correspondence to Oishila Bandyopadhyay or Sanjoy Pratihar .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Mandal, S.C., Bandyopadhyay, O., Pratihar, S. (2021). Detection of Concave Points in Closed Object Boundaries Aiming at Separation of Overlapped Objects. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1378. Springer, Singapore. https://doi.org/10.1007/978-981-16-1103-2_43

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  • DOI: https://doi.org/10.1007/978-981-16-1103-2_43

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