Adaptive structure tensors and their applications
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Adaptive structure tensors and their applications. / Brox, Thomas; Van Den Boomgaard, Rein; Lauze, François; Van De Weijer, Joost; Weickert, Joachim; Mrázek, Pavel; Kornprobst, Pierre.
In: Mathematics and Visualization, No. 200709, 2006, p. 17-47.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Adaptive structure tensors and their applications
AU - Brox, Thomas
AU - Van Den Boomgaard, Rein
AU - Lauze, François
AU - Van De Weijer, Joost
AU - Weickert, Joachim
AU - Mrázek, Pavel
AU - Kornprobst, Pierre
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2006.
PY - 2006
Y1 - 2006
N2 - The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This chapter wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the data-adaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered.
AB - The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This chapter wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the data-adaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered.
UR - http://www.scopus.com/inward/record.url?scp=84925633450&partnerID=8YFLogxK
U2 - 10.1007/3-540-31272-2_2
DO - 10.1007/3-540-31272-2_2
M3 - Conference article
AN - SCOPUS:84925633450
SP - 17
EP - 47
JO - Mathematics and Visualization
JF - Mathematics and Visualization
SN - 1612-3786
IS - 200709
T2 - Workshop on Visualization and Processing of Tensor Fields, 2004
Y2 - 18 April 2004 through 23 April 2004
ER -
ID: 262858261