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Circulant dissimilarity based shape registration for object segmentation

Circulant dissimilarity based shape registration for object segmentation

Zeng, Xunxun, Chen, Fei, Wang, Meiqing and Lai, Choi-Hong ORCID: 0000-0002-7558-6398 (2018) Circulant dissimilarity based shape registration for object segmentation. International Journal of Computer Mathematics. ISSN 0020-7160 (Print), 1029-0265 (Online) (doi:

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A shape prior based object segmentation is developed in this paper by using a shape transformation distance to constrain object contour evolution. In the proposed algorithm, the transformation distance measures the dissimilarity between two unaligned shapes by cyclic shift, which is called “circulant dissimilarity”. This dissimilarity with respect to translation and rotation of the object shape is represented by circular convolution, which could be efficiently computed by using fast Fourier transform. Given a set of training shapes, the kernel density estimate is adopted to model shape prior. By integrating low-level image feature, high-level shape prior and transformation distance, a variational segmentation model is proposed to solve the transformation invariance of shape prior. Numerical experiments demonstrate that circulant dissimilarity based shape registration outperforms the iterative optimization on explicit pose parameters, and show promising results and highlight the potential of the method for object registration
and segmentation.

Item Type: Article
Additional Information: Accepted 21 Feb 2018, Accepted author version posted online: 26 Feb 2018, Published online: 11 Mar 2018
Uncontrolled Keywords: Segmentation, Circulant dissimilarity, Shape prior, Kernel density estimation, Level set.
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 12 Mar 2019 01:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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