Image segmentation technique- a comparative study

  • Authors

    • Brunda R
    • Divyashree B
    • N. Shobha Rani
    https://doi.org/10.14419/ijet.v7i4.21552

  • Image segmentation techniques aims at identification and extraction of foreground objects in an image resulting into individual segments. Segmentation of images basically are so varied from one type of image to other images as each had its own context and varied geometrical properties and thus leading to a challenge in design of a generic algorithmic procedure. In this paper, an effort is formed to compare and study the efficiency of color image segmentation victimization color areas, watersheds, fuzzy c-means and edge detection techniques towards the segmentation of fruit images. The fruit images employed for segmentation are downloaded from various sources of online and also few of the images are synthetically gathered by capturing the fruits images over a plain background. The analysis had resulted in conclusion that performance of fuzzy c -means and watersheds had led to optimal outcomes than other techniques.

  • References

    1. [1] D. J., Gupta, A. K., & Khan, F. A. (2015). Comparing the performance of L* A*B* and HSV color spaces with respect to color image segmentation. arXiv preprint arXiv:1506.01472.

      [2] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698. https://doi.org/10.1109/TPAMI.1986.4767851.

      [3] Abdulghafour, M. (2003). Image segmentation using Fuzzy logic and genetic algorithms.

      [4] Bora, D. J., Gupta, A. K., & Khan, F. A. (2015). Color Image Segmentation Using An Efficient Fuzzy Based Watershed Approach. Signal & Image Processing an International Journal (SIPIJ), 6(5), 15-34. https://doi.org/10.5121/sipij.2015.6502.

      [5] Shanmugavadivu, P., & Kumar, A. (2014, November). Modified eight-directional canny for robust edge detection. In Contemporary Computing and Informatics (IC3I), 2014 International Conference on (pp. 751-756). IEEE.networksâ€, Proceedings of the IEEE conference on Computer Vision.

      [6] Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on pattern analysis and machine intelligence, 22(8), 888-905. https://doi.org/10.1109/34.868688.

      [7] Bruce, J., Balch, T., &Veloso, M. (2000). Fast and inexpensive color image segmentation for interactive robots. In Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on (Vol. 3, pp. 2061-2066). IEEE. https://doi.org/10.1109/IROS.2000.895274.

      [8] Felzenszwalb, P. F., &Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International journal of computer vision, 59(2), 167-181. https://doi.org/10.1023/B:VISI.0000022288.19776.77.

      [9] Vese, L. A., & Chan, T. F. (2002). A multiphase level set framework for image segmentation using the Mumford and Shah Model. International journal of computer vision, 50(3), 271-293. https://doi.org/10.1023/A:1020874308076.

      [10] Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), 629-639. https://doi.org/10.1109/34.56205.

      [11] Haralick, R. M. (1984). Digital step edges from zero crossing of second directional derivatives. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1), 58-68. https://doi.org/10.1109/TPAMI.1984.4767475.

      [12] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698. https://doi.org/10.1109/TPAMI.1986.4767851.

      [13] Young, I. T., & Van Vliet, L. J. (1995). Recursive implementation of the Gaussian filter. Signal processing, 44(2), 139-151. https://doi.org/10.1016/0165-1684(95)00020-E.

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  • How to Cite

    R, B., B, D., & Rani, N. S. (2018). Image segmentation technique- a comparative study. International Journal of Engineering & Technology, 7(4), 3131-3134. https://doi.org/10.14419/ijet.v7i4.21552