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Adaptive volumetric texture segmentation based on Gaussian Markov random fields features

Adaptive volumetric texture segmentation based on Gaussian Markov random fields features
Adaptive volumetric texture segmentation based on Gaussian Markov random fields features
An adaptive method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is extracted for each voxel in a given volumetric texture image using an estimation cube. However, the selection of the size for this estimation cube causes some fundamental issues related to the uncertainty principle and the inability of the model to capture different texture patterns. These issues are tackled here by employing an adaptive method where the size of the estimation cube is adaptively varying to capture different patterns and also minimize the number of voxels that are related to different texture classes inside the estimation cube. The feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are hence employed to segment volumetric textures. These features are smoothed by applying an averaging filter using an adaptive averaging technique. Such an averaging filter improves the segmentation results considerably. Our method proposed here is evaluated on a
synthetic volumetric texture dataset and compared with other methods to demonstrate the superiority of our segmentation method.
0167-8655
101-108
Al Makady, Yasseen Hamad
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Al Makady, Yasseen Hamad
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7

Al Makady, Yasseen Hamad, Mahmoodi, Sasan and Bennett, Michael (2020) Adaptive volumetric texture segmentation based on Gaussian Markov random fields features. Pattern Recognition Letters, 140, 101-108. (doi:10.1016/j.patrec.2020.09.035).

Record type: Article

Abstract

An adaptive method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is extracted for each voxel in a given volumetric texture image using an estimation cube. However, the selection of the size for this estimation cube causes some fundamental issues related to the uncertainty principle and the inability of the model to capture different texture patterns. These issues are tackled here by employing an adaptive method where the size of the estimation cube is adaptively varying to capture different patterns and also minimize the number of voxels that are related to different texture classes inside the estimation cube. The feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are hence employed to segment volumetric textures. These features are smoothed by applying an averaging filter using an adaptive averaging technique. Such an averaging filter improves the segmentation results considerably. Our method proposed here is evaluated on a
synthetic volumetric texture dataset and compared with other methods to demonstrate the superiority of our segmentation method.

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TextureSegmentation - Accepted Manuscript
Restricted to Repository staff only until 30 September 2021.
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More information

Accepted/In Press date: 29 September 2020
e-pub ahead of print date: 30 September 2020
Published date: December 2020

Identifiers

Local EPrints ID: 444377
URI: http://eprints.soton.ac.uk/id/eprint/444377
ISSN: 0167-8655
PURE UUID: 5871b74d-45a4-4864-b9ca-1643357c99ca
ORCID for Yasseen Hamad Al Makady: ORCID iD orcid.org/0000-0002-1583-1777

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Date deposited: 15 Oct 2020 16:30
Last modified: 18 Feb 2021 16:31

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Contributors

Author: Yasseen Hamad Al Makady ORCID iD
Author: Sasan Mahmoodi
Author: Michael Bennett

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