Similarity invariant delaunay graph matching

Dongjoe Shin; Tardi Tjahjadi

doi:10.1007/978-3-540-89689-0_7

Similarity invariant delaunay graph matching

Dongjoe Shin, Tardi Tjahjadi

University of Warwick

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

Abstract

Delaunay tessellation describes a set of arbitrarily distributed points as unique triangular graphs which preserves most local point configuration called a clique regardless of noise addition and partial occlusion. In this paper, this structure is utilised in a matching method and proposed a clique-based Hausdorff Distance (HD) to address point pattern matching problems. Since the proposed distance exploits similarity invariant features extracted from a clique, it is invariant to rotation, translation and scaling. Furthermore, it inherits noise robustness from HD and has partial matching ability because matching performs on local entities. Experimental results show that the proposed method performs better than the existing variants of the general HD.

Original language	English
Title of host publication	Structural, Syntactic, and Statistical Pattern Recognition
Subtitle of host publication	Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings
Editors	Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James T. Kwok, Michael Georgiopoulos
Publisher	Springer
Pages	25-34
ISBN (Electronic)	978-3-540-89689-0
ISBN (Print)	978-3-540-89688-3
DOIs	https://doi.org/10.1007/978-3-540-89689-0_7
Publication status	Published - Dec 2008
Externally published	Yes
Event	Structural, Syntactic and statistical pattern recognition - Orlando , United States Duration: 4 Dec 2008 → 6 Dec 2008

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	5342
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Structural, Syntactic and statistical pattern recognition
Abbreviated title	SSPR SPR 2008
Country/Territory	United States
City	Orlando
Period	4/12/08 → 6/12/08

Access to Document

10.1007/978-3-540-89689-0_7

Cite this

Shin, D., & Tjahjadi, T. (2008). Similarity invariant delaunay graph matching. In N. D. Vitoria Lobo, T. Kasparis, F. Roli, J. T. Kwok, & M. Georgiopoulos (Eds.), Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings (pp. 25-34). (Lecture Notes in Computer Science; Vol. 5342). Springer. https://doi.org/10.1007/978-3-540-89689-0_7

Shin, Dongjoe ; Tjahjadi, Tardi. / Similarity invariant delaunay graph matching. Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. editor / Niels da Vitoria Lobo ; Takis Kasparis ; Fabio Roli ; James T. Kwok ; Michael Georgiopoulos. Springer, 2008. pp. 25-34 (Lecture Notes in Computer Science).

@inproceedings{09fe5111129843f891dc99d5a2310b8d,

title = "Similarity invariant delaunay graph matching",

abstract = "Delaunay tessellation describes a set of arbitrarily distributed points as unique triangular graphs which preserves most local point configuration called a clique regardless of noise addition and partial occlusion. In this paper, this structure is utilised in a matching method and proposed a clique-based Hausdorff Distance (HD) to address point pattern matching problems. Since the proposed distance exploits similarity invariant features extracted from a clique, it is invariant to rotation, translation and scaling. Furthermore, it inherits noise robustness from HD and has partial matching ability because matching performs on local entities. Experimental results show that the proposed method performs better than the existing variants of the general HD.",

author = "Dongjoe Shin and Tardi Tjahjadi",

year = "2008",

month = dec,

doi = "10.1007/978-3-540-89689-0_7",

language = "English",

isbn = "978-3-540-89688-3",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "25--34",

editor = "{Vitoria Lobo}, {Niels da} and Takis Kasparis and Fabio Roli and Kwok, {James T.} and Michael Georgiopoulos",

booktitle = "Structural, Syntactic, and Statistical Pattern Recognition",

note = "Structural, Syntactic and statistical pattern recognition, SSPR SPR 2008 ; Conference date: 04-12-2008 Through 06-12-2008",

}

Shin, D & Tjahjadi, T 2008, Similarity invariant delaunay graph matching. in ND Vitoria Lobo, T Kasparis, F Roli, JT Kwok & M Georgiopoulos (eds), Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science, vol. 5342, Springer, pp. 25-34, Structural, Syntactic and statistical pattern recognition, Orlando , Florida, United States, 4/12/08. https://doi.org/10.1007/978-3-540-89689-0_7

Similarity invariant delaunay graph matching. / Shin, Dongjoe; Tjahjadi, Tardi.
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. ed. / Niels da Vitoria Lobo; Takis Kasparis; Fabio Roli; James T. Kwok; Michael Georgiopoulos. Springer, 2008. p. 25-34 (Lecture Notes in Computer Science; Vol. 5342).

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

TY - GEN

T1 - Similarity invariant delaunay graph matching

AU - Shin, Dongjoe

AU - Tjahjadi, Tardi

PY - 2008/12

Y1 - 2008/12

N2 - Delaunay tessellation describes a set of arbitrarily distributed points as unique triangular graphs which preserves most local point configuration called a clique regardless of noise addition and partial occlusion. In this paper, this structure is utilised in a matching method and proposed a clique-based Hausdorff Distance (HD) to address point pattern matching problems. Since the proposed distance exploits similarity invariant features extracted from a clique, it is invariant to rotation, translation and scaling. Furthermore, it inherits noise robustness from HD and has partial matching ability because matching performs on local entities. Experimental results show that the proposed method performs better than the existing variants of the general HD.

AB - Delaunay tessellation describes a set of arbitrarily distributed points as unique triangular graphs which preserves most local point configuration called a clique regardless of noise addition and partial occlusion. In this paper, this structure is utilised in a matching method and proposed a clique-based Hausdorff Distance (HD) to address point pattern matching problems. Since the proposed distance exploits similarity invariant features extracted from a clique, it is invariant to rotation, translation and scaling. Furthermore, it inherits noise robustness from HD and has partial matching ability because matching performs on local entities. Experimental results show that the proposed method performs better than the existing variants of the general HD.

U2 - 10.1007/978-3-540-89689-0_7

DO - 10.1007/978-3-540-89689-0_7

M3 - Conference contribution

SN - 978-3-540-89688-3

T3 - Lecture Notes in Computer Science

SP - 25

EP - 34

BT - Structural, Syntactic, and Statistical Pattern Recognition

A2 - Vitoria Lobo, Niels da

A2 - Kasparis, Takis

A2 - Roli, Fabio

A2 - Kwok, James T.

A2 - Georgiopoulos, Michael

PB - Springer

T2 - Structural, Syntactic and statistical pattern recognition

Y2 - 4 December 2008 through 6 December 2008

ER -

Shin D, Tjahjadi T. Similarity invariant delaunay graph matching. In Vitoria Lobo ND, Kasparis T, Roli F, Kwok JT, Georgiopoulos M, editors, Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Springer. 2008. p. 25-34. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-89689-0_7

Similarity invariant delaunay graph matching

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