Circulant dissimilarity-based shape registration for object segmentation

ABSTRACT

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 transformation 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 estimation 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.

Keywords:

• Segmentation
• circulant dissimilarity
• shape prior
• kernel density estimation
• level set

2010 AMS SUBJECT CLASSIFICATIONS:

• 65D18
• 68U10
• 62G07

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Fei Chen http://orcid.org/0000-0002-3676-6011

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [61401098, 61771141, 61473330], and the Natural Science Foundation of Fujian Province [2017J01751].