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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00225691%21RIV15-MSM-21230___
rdf:type
n18:Vysledek skos:Concept
dcterms:description
This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns. The only assumption made of the scene geometry is that repeated scene elements are mapped to each other by planar Euclidean transformations. The class of patterns covered is broad and includes nearly all commonly seen, planar, man-made repeated patterns. In addition, novel linear constraints are used to reduce geometric ambiguity between the rectified imaged pattern and the scene pattern. Rectification to within a similarity of the scene plane is achieved from one rotated repeat, or to within a similarity with a scale ambiguity along the axis of symmetry from one reflected repeat. A stratum of constraints is derived that gives the necessary configuration of repeats for each successive level of rectification. A generative model for the imaged pattern is inferred and used to segment the pattern with pixel accuracy. Qualitative results are shown on a broad range of image types on which state-of-the-art methods fail. This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns. The only assumption made of the scene geometry is that repeated scene elements are mapped to each other by planar Euclidean transformations. The class of patterns covered is broad and includes nearly all commonly seen, planar, man-made repeated patterns. In addition, novel linear constraints are used to reduce geometric ambiguity between the rectified imaged pattern and the scene pattern. Rectification to within a similarity of the scene plane is achieved from one rotated repeat, or to within a similarity with a scale ambiguity along the axis of symmetry from one reflected repeat. A stratum of constraints is derived that gives the necessary configuration of repeats for each successive level of rectification. A generative model for the imaged pattern is inferred and used to segment the pattern with pixel accuracy. Qualitative results are shown on a broad range of image types on which state-of-the-art methods fail.
dcterms:title
Rectification, and Segmentation of Coplanar Repeated Patterns Rectification, and Segmentation of Coplanar Repeated Patterns
skos:prefLabel
Rectification, and Segmentation of Coplanar Repeated Patterns Rectification, and Segmentation of Coplanar Repeated Patterns
skos:notation
RIV/68407700:21230/14:00225691!RIV15-MSM-21230___
n4:aktivita
n16:P
n4:aktivity
P(GAP103/12/2310), P(GBP103/12/G084), P(LL1303)
n4:dodaniDat
n21:2015
n4:domaciTvurceVysledku
n9:7004818 Pritts, James Brandon n9:1711326
n4:druhVysledku
n5:D
n4:duvernostUdaju
n17:S
n4:entitaPredkladatele
n15:predkladatel
n4:idSjednocenehoVysledku
41656
n4:idVysledku
RIV/68407700:21230/14:00225691
n4:jazykVysledku
n11:eng
n4:klicovaSlova
rectification; repeated patterns; segmentation
n4:klicoveSlovo
n7:segmentation n7:repeated%20patterns n7:rectification
n4:kontrolniKodProRIV
[8F564D4AAB10]
n4:mistoKonaniAkce
Columbus, Ohio
n4:mistoVydani
Los Alamitos
n4:nazevZdroje
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
n4:obor
n14:JD
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n6:GBP103%2F12%2FG084 n6:GAP103%2F12%2F2310 n6:LL1303
n4:rokUplatneniVysledku
n21:2014
n4:tvurceVysledku
Pritts, James Brandon Chum, Ondřej Matas, Jiří
n4:typAkce
n8:WRD
n4:zahajeniAkce
2014-06-23+02:00
s:issn
1063-6919
s:numberOfPages
8
n20:doi
10.1109/CVPR.2014.380
n13:hasPublisher
IEEE Computer Society Press
n19:isbn
978-1-4799-5117-8
n22:organizacniJednotka
21230