Lattice Metric Space Application to Grain Defect Detection

Yuchen He; Sung Ha Kang

doi:10.1007/978-3-030-22368-7_30

Lattice Metric Space Application to Grain Defect Detection

Yuchen He^*, Sung Ha Kang

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

We propose a new model for grain defect detection based on the theory of lattice metric space [7]. The lattice metric space (L, d_L) shows outstanding advantages in representing lattices. Utilizing this advantage, we propose a new algorithm, Lattice clustering algorithm (LCA). After over-segmentation using regularized k-means, the merging stage is built upon the lattice equivalence relation. Since LCA is built upon (L, d_L), it is robust against missing particles, deficient hexagonal cells, and can handle non-hexagonal lattices without any modification. We present various numerical experiments to validate our method and investigate interesting properties. © 2019, Springer Nature Switzerland AG.

Original language	English
Title of host publication	Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings
Editors	Jan Lellmann, Martin Burger, Jan Modersitzki
Publisher	Springer, Cham
Pages	381-392
ISBN (Electronic)	9783030223687
ISBN (Print)	9783030223670
DOIs	https://doi.org/10.1007/978-3-030-22368-7_30
Publication status	Published - 2019
Externally published	Yes
Event	7th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019) - Conference Center Hofgeismar, Hofgeismar, Germany Duration: 30 Jun 2019 → 4 Jul 2019 http://ssvm2019.mic.uni-luebeck.de/

Publication series

Name	Lecture Notes in Computer Science
Volume	11603
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019)
Abbreviated title	SSVM2019
Place	Germany
City	Hofgeismar
Period	30/06/19 → 4/07/19
Internet address	http://ssvm2019.mic.uni-luebeck.de/

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He, Y., & Kang, S. H. (2019). Lattice Metric Space Application to Grain Defect Detection. In J. Lellmann, M. Burger, & J. Modersitzki (Eds.), Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings (pp. 381-392). (Lecture Notes in Computer Science; Vol. 11603). Springer, Cham. https://doi.org/10.1007/978-3-030-22368-7_30

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title = "Lattice Metric Space Application to Grain Defect Detection",

abstract = "We propose a new model for grain defect detection based on the theory of lattice metric space [7]. The lattice metric space (L, dL) shows outstanding advantages in representing lattices. Utilizing this advantage, we propose a new algorithm, Lattice clustering algorithm (LCA). After over-segmentation using regularized k-means, the merging stage is built upon the lattice equivalence relation. Since LCA is built upon (L, dL), it is robust against missing particles, deficient hexagonal cells, and can handle non-hexagonal lattices without any modification. We present various numerical experiments to validate our method and investigate interesting properties. {\textcopyright} 2019, Springer Nature Switzerland AG.",

author = "Yuchen He and Kang, {Sung Ha}",

year = "2019",

doi = "10.1007/978-3-030-22368-7_30",

language = "English",

isbn = "9783030223670",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

pages = "381--392",

editor = "Jan Lellmann and Martin Burger and Jan Modersitzki",

booktitle = "Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings",

note = "7th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019) , SSVM2019 ; Conference date: 30-06-2019 Through 04-07-2019",

url = "http://ssvm2019.mic.uni-luebeck.de/",

}

He, Y & Kang, SH 2019, Lattice Metric Space Application to Grain Defect Detection. in J Lellmann, M Burger & J Modersitzki (eds), Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. Lecture Notes in Computer Science, vol. 11603, Springer, Cham, pp. 381-392, 7th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019) , Hofgeismar, Germany, 30/06/19. https://doi.org/10.1007/978-3-030-22368-7_30

Lattice Metric Space Application to Grain Defect Detection. / He, Yuchen; Kang, Sung Ha.
Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. ed. / Jan Lellmann; Martin Burger; Jan Modersitzki. Springer, Cham, 2019. p. 381-392 (Lecture Notes in Computer Science; Vol. 11603).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Lattice Metric Space Application to Grain Defect Detection

AU - He, Yuchen

AU - Kang, Sung Ha

PY - 2019

Y1 - 2019

N2 - We propose a new model for grain defect detection based on the theory of lattice metric space [7]. The lattice metric space (L, dL) shows outstanding advantages in representing lattices. Utilizing this advantage, we propose a new algorithm, Lattice clustering algorithm (LCA). After over-segmentation using regularized k-means, the merging stage is built upon the lattice equivalence relation. Since LCA is built upon (L, dL), it is robust against missing particles, deficient hexagonal cells, and can handle non-hexagonal lattices without any modification. We present various numerical experiments to validate our method and investigate interesting properties. © 2019, Springer Nature Switzerland AG.

AB - We propose a new model for grain defect detection based on the theory of lattice metric space [7]. The lattice metric space (L, dL) shows outstanding advantages in representing lattices. Utilizing this advantage, we propose a new algorithm, Lattice clustering algorithm (LCA). After over-segmentation using regularized k-means, the merging stage is built upon the lattice equivalence relation. Since LCA is built upon (L, dL), it is robust against missing particles, deficient hexagonal cells, and can handle non-hexagonal lattices without any modification. We present various numerical experiments to validate our method and investigate interesting properties. © 2019, Springer Nature Switzerland AG.

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U2 - 10.1007/978-3-030-22368-7_30

DO - 10.1007/978-3-030-22368-7_30

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783030223670

T3 - Lecture Notes in Computer Science

SP - 381

EP - 392

BT - Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings

A2 - Lellmann, Jan

A2 - Burger, Martin

A2 - Modersitzki, Jan

PB - Springer, Cham

T2 - 7th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019)

Y2 - 30 June 2019 through 4 July 2019

ER -

Lattice Metric Space Application to Grain Defect Detection

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