DocumentCode :
639484
Title :
As-Projective-As-Possible Image Stitching with Moving DLT
Author :
Zaragoza, Jordi ; Tat-Jun Chin ; Brown, Michael S. ; Suter, David
Author_Institution :
Australian Centre for Visual Technol., Univ. of Adelaide, Adelaide, SA, Australia
fYear :
2013
fDate :
23-28 June 2013
Firstpage :
2339
Lastpage :
2346
Abstract :
We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp - a model that is justified when the scene is planar or when the views differ purely by rotation. Such conditions are easily violated in practice, and this yields stitching results with ghosting artefacts that necessitate the usage of deghosting algorithms. To this end we propose as-projective-as-possible warps, i.e., warps that aim to be globally projective, yet allow local non-projective deviations to account for violations to the assumed imaging conditions. Based on a novel estimation technique called Moving Direct Linear Transformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with the projective model. The result is highly accurate image stitching, with significantly reduced ghosting effects, thus lowering the dependency on post hoc deghosting.
Keywords :
image processing; motion estimation; Moving DLT; as-projective-as-possible image stitching; as-projective-as-possible warps; deghosting algorithms; estimation technique; ghosting artefacts; image regions; imaging conditions; model inadequacies; moving DLT; moving direct linear transformation; post hoc deghosting; projective estimation; projective model; projective warp; Cameras; Coplanar waveguides; Data models; Estimation; Extrapolation; Transmission line matrix methods; Vectors; DLT; Image Stitching; Moving Least Squares;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
ISSN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2013.303
Filename :
6619147
Link To Document :
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