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Hybrid Active Contour Model for Segmentation of Synthetic and Real Images

EasyChair Preprint no. 6759

2 pagesDate: October 3, 2021

Abstract

Level set models are extensively used for image segmentation because of their capability to handle topological changes. In this paper, the proposed model uses combined local image information and global image information to evolve the contour around the object boundary, making it robust, irrespective of the inhomogeneity. The proposed model is capable to deal with bias conditions, such as intensity inhomogeneity and light effects. We test this model on synthetic, and real images, confirming its superiority over previous models.

Keyphrases: active contour, active contours, image processing, image segmentation, level set, Local Binary Fitting Energy, Real Image

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:6759,
  author = {Ehtesham Iqbal and Asim Niaz and Asad Munir and Kwang Nam Choi},
  title = {Hybrid Active Contour Model for Segmentation of Synthetic and Real Images},
  howpublished = {EasyChair Preprint no. 6759},

  year = {EasyChair, 2021}}
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