| 摘要: | 摘要： This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.
其他題名： Machine Vision and Applications
出版者： Berlin/Heidelberg: Springer-Verlag
出版日期： 2013-07-01
出處： Machine vision and applications, 2013-07, Vol.24 (5), p.1075-1091
資源來源： Springer Nature Link Journals
版權： Springer-Verlag Berlin Heidelberg 2013
版權： 2014 INIST-CNRS
版權： Machine Vision and Applications is a copyright of Springer, (2013). All Rights Reserved.
識別號： ISSN: 0932-8092
識別號： EISSN: 1432-1769
識別號： DOI: 10.1007/s00138-013-0504-2
識別號： CODEN: MVAPEO |
