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A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model

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Abstract

In this paper, a novel approach to texture segmentation based on the parametric active contour model (ACM) is proposed. At first, gray-level co-occurrence matrix and subsequently co-occurrence energy of the regions inside and outside of the dynamic contour are calculated. Difference of this energy corresponding to both the regions is used as the external energy of the proposed ACM. The contour stops and converges completely when this difference attains a maximum value. The proposed approach requires only initial contour selection and no object point selection like the other variants of parametric ACM used for texture segmentation. Experiments on a number of synthetic and real-world texture images show that in all cases, we are getting a better segmentation of the object although for few cases the execution time is bit more than that of other existing methods.

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References

  1. Karasev, P., Kolesov, I., et al.: Interactive medical image segmentation using PDE control of active contours. IEEE Trans. Med. Imaging 32(11), 2127–2139 (2013)

    Article  Google Scholar 

  2. Liu, G., Sun, X., et al.: Interactive geospatial object extraction in high resolution remote sensing images using shape-based global minimization active contour model. Pattern Recognit. Lett. 34(10), 1186–1195 (2013)

    Article  Google Scholar 

  3. Tatu, A., Bansal, S.: A novel active contour model for texture segmentation. In: International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer (2015)

  4. Cimpoi, M., Maji, S., Vedaldi, A.: Deep filter banks for texture recognition and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

  5. Andrearczyk, V., Whelan, P.F.: Texture segmentation with fully convolutional networks. arXiv preprint arXiv:1703.05230 (2017)

  6. Kass, M., Witkin, A., et al.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  Google Scholar 

  7. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  8. Li, B., Acton, S.T.: Active contour external force using vector field convolution for image segmentation. IEEE Trans. Image Process. 16(8), 2096–2106 (2007)

    Article  MathSciNet  Google Scholar 

  9. Caselles, V., Kimmel, R., et al.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  10. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  11. Houhou, N., Thiran, J.-P.,et al.: Fast texture segmentation model based on the shape operator and active contour. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)

  12. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vis. 46(3), 223–247 (2002)

    Article  MATH  Google Scholar 

  13. Sagiv, C., Sochen, N.A., Zeevi, Y.Y.: Integrated active contours for texture segmentation. IEEE Trans. Image Process. 15(6), 1633–1646 (2006)

    Article  Google Scholar 

  14. Qinggang, W., Gan, Y., Lin, B., Zhang, Q., Chang, H.: An active contour model based on fused texture features for image segmentation. Neurocomputing 151, 1133–1141 (2015)

    Article  Google Scholar 

  15. Fergani, K., Lui, D.: Hybrid structural and texture distinctiveness vector field convolution for region segmentation. Comput. Vis. Image Underst. 125, 85–96 (2014)

    Article  Google Scholar 

  16. Chan, D.-Y., et al.: Rectification-conducted adaptive snake for segmenting complex-boundary objects from textured backgrounds. Signal Image Video Process. 10(2), 225–234 (2016)

    Article  MathSciNet  Google Scholar 

  17. Vard, A.R., Moallem, P., et al.: Texture-based parametric active contour for target detection and tracking. Int. J. Imaging Syst. Technol. 19(3), 187–198 (2009)

    Article  Google Scholar 

  18. Vard, A.R., Monadjemi, A., et al.: Fast texture energy based image segmentation using directional Walsh Hadamard transform and parametric active contour models. Expert Syst. Appl. 38(9), 11722–11729 (2011)

    Article  Google Scholar 

  19. Moallem, P., Tahvilian, H., et al.: Parametric active contour model using Gabor balloon energy for texture segmentation. Signal Image Video Process. 10(2), 351–358 (2016)

    Article  Google Scholar 

  20. Bigun, J.: Speed, frequency, and orientation tuned 3-d gabor filter banks and their design. In: Proceedings of the 12th IAPR International Conference Pattern Recognition, vol. 3-Conference C Signal Processing, pp. 184–187 (1994)

  21. Materka, A., Strzelecki, M.: Texture analysis methods: a review, Technical University of Lodz, Institute of Electronics, COST B11 Report, Brussels, pp. 9–11 (1998)

  22. Zhou, H., Zheng, J., Wei, L.: Texture aware image segmentation using graph cuts and active contours. Pattern Recognit. 46(6), 1719–1733 (2013)

    Article  MATH  Google Scholar 

  23. Subudhi, P., Mukhopadhyay, S.: A fast texture segmentation scheme based on active contours and discrete cosine transform. Comput. Electr. Eng. 62, 105–118 (2017)

  24. Subudhi, P., Mukhopadhyay, S.: A pyramidal approach to active contours implementation for 2D gray scale image segmentation. In: International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE (2016)

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India

    Priyambada Subudhi

  2. Indian Institute of Technology (ISM), Dhanbad, India

    Susanta Mukhopadhyay

Authors
  1. Priyambada Subudhi
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  2. Susanta Mukhopadhyay
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Correspondence to Priyambada Subudhi.

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Subudhi, P., Mukhopadhyay, S. A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model. SIViP 12, 669–676 (2018). https://doi.org/10.1007/s11760-017-1206-4

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  • DOI: https://doi.org/10.1007/s11760-017-1206-4

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