Compression and Querying of Arbitrary Geodesic Distances

Aiello, R; Banterle, F; Pietroni, N; Malomo, L; Cignoni, P; Scopigno, R

Compression and Querying of Arbitrary Geodesic Distances

Aiello, R Banterle, F Pietroni, N

Malomo, L Cignoni, P Scopigno, R

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Publisher:: SPRINGER INTERNATIONAL PUBLISHING AG
Publication Type:: Conference Proceeding
Citation:: International Conference on Image Analysis and Processing, 2015, 9279, pp. 282-293
Issue Date:: 2015

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Field	Value	Language
dc.contributor.author	Aiello, R
dc.contributor.author	Banterle, F
dc.contributor.author	Pietroni, N https://orcid.org/0000-0002-8271-2102
dc.contributor.author	Malomo, L
dc.contributor.author	Cignoni, P
dc.contributor.author	Scopigno, R
dc.contributor.editor	Murino, V
dc.contributor.editor	Puppo, E
dc.date	2015-09-07
dc.date.accessioned	2023-03-17T03:56:55Z
dc.date.available	2023-03-17T03:56:55Z
dc.date.issued	2015
dc.identifier.citation	International Conference on Image Analysis and Processing, 2015, 9279, pp. 282-293
dc.identifier.isbn	9783319232300
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/167443
dc.description.abstract	In this paper, we propose a novel method for accelerating the computation of geodesic distances over arbitrary manifold triangulated surfaces. The method is based on a preprocessing step where we build a data structure. This allows to store arbitrary complex distance metrics. We show that, by exploiting the precomputed data, the proposed method is significantly faster than the classical Dijkstra algorithm for the computation of point to point distances. Moreover, as we precompute exact geodesic distances, the proposed approach can be more accurate than state-of-the-art approximations.
dc.language	en
dc.publisher	SPRINGER INTERNATIONAL PUBLISHING AG
dc.relation.ispartof	International Conference on Image Analysis and Processing
dc.relation.ispartof	18th International Conference on Image Analysis and Processing (ICIAP)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-319-23231-7_26
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Compression and Querying of Arbitrary Geodesic Distances
dc.type	Conference Proceeding
utslib.citation.volume	9279
utslib.location.activity	Springer, Cham
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-17T03:56:53Z
pubs.finish-date	2015-09-11
pubs.publication-status	Published
pubs.start-date	2015-09-07
pubs.volume	9279

Abstract:

In this paper, we propose a novel method for accelerating the computation of geodesic distances over arbitrary manifold triangulated surfaces. The method is based on a preprocessing step where we build a data structure. This allows to store arbitrary complex distance metrics. We show that, by exploiting the precomputed data, the proposed method is significantly faster than the classical Dijkstra algorithm for the computation of point to point distances. Moreover, as we precompute exact geodesic distances, the proposed approach can be more accurate than state-of-the-art approximations.

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http://hdl.handle.net/10453/167443