Hybrid Active Contour Model for Segmentation of Synthetic and Real Images

Iqbal, E.; Niaz, A.; Munir, A.; Choi, Kwang Nam

doi:10.1109/ISPACS51563.2021.9651047

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Hybrid Active Contour Model for Segmentation of Synthetic and Real Images

Iqbal, E.;
Niaz, A.;
Munir, A.;
Choi, Kwang Nam

Citations

SCOPUS

1

초록

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 con-tour 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. © 2021 IEEE.

키워드

Image segmentation; Level set

제목: Hybrid Active Contour Model for Segmentation of Synthetic and Real Images

저자: Iqbal, E.; Niaz, A.; Munir, A.; Choi, Kwang Nam

DOI: 10.1109/ISPACS51563.2021.9651047

발행일: 2021-11

유형: Conference Paper

저널명: ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding

언어: ENG

출판사: Institute of Electrical and Electronics Engineers Inc.

ISSN: P 0000-0000