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Gaussian Markov random fields-based features for volumetric texture segmentation

Gaussian Markov random fields-based features for volumetric texture segmentation
Gaussian Markov random fields-based features for volumetric texture segmentation
A new method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation (VTS). A feature vector is extracted for each voxel in a given volumetric texture image. These feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are employed to segment volumetric textures. To overcome the issues related to boundaries and isolated voxels, a solution is proposed by sliding an averaging volume inside the parameter volume to assign each voxel a new feature vector derived as the mean of the surrounding voxels that are collected by the averaging volume. Our proposed method is evaluated on a synthetic volumetric texture and compared with another method demonstrating good segmentation performance. A further evaluation is carried out to examine the performance of the method proposed here in the presence of noise to show robustness to noise.
IEEE
Almakady, Yasseen
f183c2d0-f78f-4c38-a9db-0fa8e7054300
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
36d933d9-330d-4c97-952a-3a18e034d500
Almakady, Yasseen
f183c2d0-f78f-4c38-a9db-0fa8e7054300
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
36d933d9-330d-4c97-952a-3a18e034d500

Almakady, Yasseen, Mahmoodi, Sasan and Bennett, Michael (2019) Gaussian Markov random fields-based features for volumetric texture segmentation. In Proceedings of IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE. 4 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

A new method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation (VTS). A feature vector is extracted for each voxel in a given volumetric texture image. These feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are employed to segment volumetric textures. To overcome the issues related to boundaries and isolated voxels, a solution is proposed by sliding an averaging volume inside the parameter volume to assign each voxel a new feature vector derived as the mean of the surrounding voxels that are collected by the averaging volume. Our proposed method is evaluated on a synthetic volumetric texture and compared with another method demonstrating good segmentation performance. A further evaluation is carried out to examine the performance of the method proposed here in the presence of noise to show robustness to noise.

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More information

Accepted/In Press date: 11 December 2018
Published date: 1 March 2019
Venue - Dates: IEEE International Conference on Multimedia Information Processing and Retrieval, San Jose, United States, 2019-03-28 - 2019-03-30

Identifiers

Local EPrints ID: 426777
URI: https://eprints.soton.ac.uk/id/eprint/426777
PURE UUID: d453f843-bda3-48b0-9141-76a0089cce37

Catalogue record

Date deposited: 12 Dec 2018 17:30
Last modified: 12 Dec 2018 17:30

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