Impact of Threshold in Gray Level Slicing and Seeded Region Growing Segmentation

  • Authors

    • Mrs. S.Vanithamai
    • Dr. S.Purushothaman
    https://doi.org/10.14419/ijet.v7i3.10.25369
  • Segmentation, Gray Level Slicing, Seeded Region Growing, Threshold, Region of interest, Centroid, Vehicle detection, Tracking, Motion Analysis

  • Abstract

    Traffic surveillance is the most important area of research which involves the safety issues in driving. Video Sequences are taken and it is converted as image sequences. By calculating the centroid of the image the image sequences are analyzed. The centroid value has been chosen as seed point in the image. From the seed point the region is expanded using the seeded region growing algorithm. Similarities of the pixels are considered within neighborhood pixels and based on the threshold value the segmentation have been done to identify the object. Gray level slicing method is used to identify the vehicles in the images. When it is compare, the seeded growing method the efficiency of the segmentation in image sequences is improved.

     

  • References

    1. [1] K. Krishan, S. Singh, “Color image segmentation using improved region growing and k-means methodâ€, IOSR Journal of Engineering (IOSRJEN). Vol. 04, Issue 05 (May. 2014), ||V4|| PP 43-46

      [2] P. K. Jain and S. Susan, “An adaptive single seed based region growing algorithm for color image segmentationâ€, 2013 Annual IEEE India Conference (INDICON).

      [3] R. Sharma, R. Sharma,“Image segmentation using morphological operation for automatic region growingâ€, CTIEMT, Jalandhar, PTU, Punjab, India. Vol. 2, Issue 9, September 2014.

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  • How to Cite

    S.Vanithamai, M., & S.Purushothaman, D. (2018). Impact of Threshold in Gray Level Slicing and Seeded Region Growing Segmentation. International Journal of Engineering & Technology, 7(3.10), 227-229. https://doi.org/10.14419/ijet.v7i3.10.25369

    Received date: 2019-01-04

    Accepted date: 2019-01-04