Physics based modeling for the development of soft segmentation and reconstruction algorithms

Amirkoushyar Ziabari; Jeffrey Rickman; Jeffrey Simmons; Charles A. Bouman

doi:10.1109/ACSSC.2017.8335688

Physics based modeling for the development of soft segmentation and reconstruction algorithms

Amirkoushyar Ziabari, Jeffrey Rickman, Jeffrey Simmons, Charles A. Bouman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We propose a new regularization approach, using a soft-spin, phase-field model prior, that incorporates physics-based parameter estimates for joint soft segmentation and for reconstruction algorithms that avoid discontinuities between region labels. The phase-field model employed here is based on a continuous, coarse-grained field variable describing distinct crystallites having different orientations that comprise a poly-crystalline microstructure. The proposed algorithm, called Snap is used here to perform joint segmentation and reconstruction of materials microstructures from several noisy numerical measurements. Comparisons with state-of-the-art de-noising algorithms demonstrate the superior performance of Snap.

Original language	English
Title of host publication	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1875-1880
Number of pages	6
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335688
State	Published - Jul 2 2017
Externally published	Yes
Event	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States Duration: Oct 29 2017 → Nov 1 2017

Publication series

Name	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume	2017-October

Conference

Conference	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/Territory	United States
City	Pacific Grove
Period	10/29/17 → 11/1/17

Keywords

Physics-based prior
Simplex
Snap potential
soft segmentation

Access to Document

10.1109/ACSSC.2017.8335688

Cite this

Ziabari, A., Rickman, J., Simmons, J., & Bouman, C. A. (2017). Physics based modeling for the development of soft segmentation and reconstruction algorithms. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 1875-1880). (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335688

Ziabari, Amirkoushyar ; Rickman, Jeffrey ; Simmons, Jeffrey et al. / Physics based modeling for the development of soft segmentation and reconstruction algorithms. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1875-1880 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

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abstract = "We propose a new regularization approach, using a soft-spin, phase-field model prior, that incorporates physics-based parameter estimates for joint soft segmentation and for reconstruction algorithms that avoid discontinuities between region labels. The phase-field model employed here is based on a continuous, coarse-grained field variable describing distinct crystallites having different orientations that comprise a poly-crystalline microstructure. The proposed algorithm, called Snap is used here to perform joint segmentation and reconstruction of materials microstructures from several noisy numerical measurements. Comparisons with state-of-the-art de-noising algorithms demonstrate the superior performance of Snap.",

keywords = "Physics-based prior, Simplex, Snap potential, soft segmentation",

author = "Amirkoushyar Ziabari and Jeffrey Rickman and Jeffrey Simmons and Bouman, {Charles A.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 ; Conference date: 29-10-2017 Through 01-11-2017",

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language = "English",

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Ziabari, A, Rickman, J, Simmons, J & Bouman, CA 2017, Physics based modeling for the development of soft segmentation and reconstruction algorithms. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 1875-1880, 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Pacific Grove, United States, 10/29/17. https://doi.org/10.1109/ACSSC.2017.8335688

Physics based modeling for the development of soft segmentation and reconstruction algorithms. / Ziabari, Amirkoushyar; Rickman, Jeffrey; Simmons, Jeffrey et al.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1875-1880 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We propose a new regularization approach, using a soft-spin, phase-field model prior, that incorporates physics-based parameter estimates for joint soft segmentation and for reconstruction algorithms that avoid discontinuities between region labels. The phase-field model employed here is based on a continuous, coarse-grained field variable describing distinct crystallites having different orientations that comprise a poly-crystalline microstructure. The proposed algorithm, called Snap is used here to perform joint segmentation and reconstruction of materials microstructures from several noisy numerical measurements. Comparisons with state-of-the-art de-noising algorithms demonstrate the superior performance of Snap.

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Ziabari A, Rickman J, Simmons J, Bouman CA. Physics based modeling for the development of soft segmentation and reconstruction algorithms. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1875-1880. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335688

Physics based modeling for the development of soft segmentation and reconstruction algorithms

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