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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: Xianghua, Xie

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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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first_indexed 2015-07-02T02:07:57Z
last_indexed 2021-01-29T03:37:09Z
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spelling 2021-01-28T16:08:41.3785854 v2 22242 2015-07-01 Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2015-07-01 SCS 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. Journal Article IEEE Transactions on Image Processing 24 11 3902 3914 Image segmentation, deformable model, initialisation invariance 30 11 2015 2015-11-30 10.1109/TIP.2015.2456503 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-01-28T16:08:41.3785854 2015-07-01T11:01:02.9181315 College of Science Computer Science Huaizhong Zhang 1 Xianghua Xie 0000-0002-2701-8660 2
title Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
spellingShingle Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
Xianghua, Xie
title_short Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
title_full Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
title_fullStr Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
title_full_unstemmed Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
title_sort Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
author Xianghua, Xie
author2 Huaizhong Zhang
Xianghua Xie
format Journal article
container_title IEEE Transactions on Image Processing
container_volume 24
container_issue 11
container_start_page 3902
publishDate 2015
institution Swansea University
doi_str_mv 10.1109/TIP.2015.2456503
college_str College of Science
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hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
document_store_str 0
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description 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.
published_date 2015-11-30T03:37:04Z
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score 10.79113