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Conference Paper: TILT: Transform invariant low-rank textures

TitleTILT: Transform invariant low-rank textures
Authors
Issue Date2011
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, v. 6494 LNCS, n. PART 3, p. 314-328 How to Cite?
AbstractIn this paper, we show how to efficiently and effectively extract a rich class of low-rank textures in a 3D scene from 2D images despite significant distortion and warping. The low-rank textures capture geometrically meaningful structures in an image, which encompass conventional local features such as edges and corners as well as all kinds of regular, symmetric patterns ubiquitous in urban environments and man-made objects. Our approach to finding these low-rank textures leverages the recent breakthroughs in convex optimization that enable robust recovery of a high-dimensional low-rank matrix despite gross sparse errors. In the case of planar regions with significant projective deformation, our method can accurately recover both the intrinsic low-rank texture and the precise domain transformation. Extensive experimental results demonstrate that this new technique works effectively for many near-regular patterns or objects that are approximately low-rank, such as human faces and text. © 2011 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/327475
ISSN
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zhengdong-
dc.contributor.authorLiang, Xiao-
dc.contributor.authorGanesh, Arvind-
dc.contributor.authorMa, Yi-
dc.date.accessioned2023-03-31T05:31:36Z-
dc.date.available2023-03-31T05:31:36Z-
dc.date.issued2011-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, v. 6494 LNCS, n. PART 3, p. 314-328-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/327475-
dc.description.abstractIn this paper, we show how to efficiently and effectively extract a rich class of low-rank textures in a 3D scene from 2D images despite significant distortion and warping. The low-rank textures capture geometrically meaningful structures in an image, which encompass conventional local features such as edges and corners as well as all kinds of regular, symmetric patterns ubiquitous in urban environments and man-made objects. Our approach to finding these low-rank textures leverages the recent breakthroughs in convex optimization that enable robust recovery of a high-dimensional low-rank matrix despite gross sparse errors. In the case of planar regions with significant projective deformation, our method can accurately recover both the intrinsic low-rank texture and the precise domain transformation. Extensive experimental results demonstrate that this new technique works effectively for many near-regular patterns or objects that are approximately low-rank, such as human faces and text. © 2011 Springer-Verlag Berlin Heidelberg.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.titleTILT: Transform invariant low-rank textures-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-19318-7_25-
dc.identifier.scopuseid_2-s2.0-79952522262-
dc.identifier.volume6494 LNCS-
dc.identifier.issuePART 3-
dc.identifier.spage314-
dc.identifier.epage328-
dc.identifier.eissn1611-3349-

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