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Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations / Huaizhong Zhang; Xianghua Xie

IEEE Transactions on Image Processing, Volume: 24, Issue: 11, Pages: 3902 - 3914

Swansea University Author: Xie, Xianghua

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DOI (Published version): 10.1109/TIP.2015.2456503

Abstract

We propose a unified approach to deformable model based segmentation. The fundamental force field of the proposed method is based on computing the divergence of a gradient convolution field (GCF), which makes full use of directional information of the image gradient vectors and their interactions ac...

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Published in: IEEE Transactions on Image Processing
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa22242
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Abstract: We propose a unified approach to deformable model based segmentation. The fundamental force field of the proposed method is based on computing the divergence of a gradient convolution field (GCF), which makes full use of directional information of the image gradient vectors and their interactions across image domain. The proposed external force field for deformable segmentation has both edge-based properties in that GCF is computed from image gradients, and region-based attributes since its divergence can be treated as a region indication function. Moreover, nonlinear diffusion can be conveniently applied to GCF to improve its performance in dealing with noise interference. We also show the extension of GCF from 2-D to 3-D.
Keywords: Image segmentation, deformable model, initialisation invariance
College: College of Science
Issue: 11
Start Page: 3902
End Page: 3914