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  1. Journal of Mathematical Imaging and Vision
  2. Journal of Mathematical Imaging and Vision : Volume 39
  3. Journal of Mathematical Imaging and Vision : Volume 39, Issue 2, February 2011
  4. Rank Estimation in Missing Data Matrix Problems
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Journal of Mathematical Imaging and Vision : Volume 58
Journal of Mathematical Imaging and Vision : Volume 57
Journal of Mathematical Imaging and Vision : Volume 56
Journal of Mathematical Imaging and Vision : Volume 55
Journal of Mathematical Imaging and Vision : Volume 54
Journal of Mathematical Imaging and Vision : Volume 53
Journal of Mathematical Imaging and Vision : Volume 52
Journal of Mathematical Imaging and Vision : Volume 51
Journal of Mathematical Imaging and Vision : Volume 50
Journal of Mathematical Imaging and Vision : Volume 49
Journal of Mathematical Imaging and Vision : Volume 48
Journal of Mathematical Imaging and Vision : Volume 47
Journal of Mathematical Imaging and Vision : Volume 46
Journal of Mathematical Imaging and Vision : Volume 45
Journal of Mathematical Imaging and Vision : Volume 44
Journal of Mathematical Imaging and Vision : Volume 43
Journal of Mathematical Imaging and Vision : Volume 42
Journal of Mathematical Imaging and Vision : Volume 41
Journal of Mathematical Imaging and Vision : Volume 40
Journal of Mathematical Imaging and Vision : Volume 39
Journal of Mathematical Imaging and Vision : Volume 39, Issue 3, March 2011
Journal of Mathematical Imaging and Vision : Volume 39, Issue 2, February 2011
Morphological Amoebas Are Self-snakes
A Novel Kernel Correlation Model with the Correspondence Estimation
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform
Rank Estimation in Missing Data Matrix Problems
Periodic Plus Smooth Image Decomposition
Moments-Based Fast Wedgelet Transform
Journal of Mathematical Imaging and Vision : Volume 39, Issue 1, January 2011
Journal of Mathematical Imaging and Vision : Volume 38
Journal of Mathematical Imaging and Vision : Volume 37
Journal of Mathematical Imaging and Vision : Volume 36
Journal of Mathematical Imaging and Vision : Volume 35
Journal of Mathematical Imaging and Vision : Volume 34
Journal of Mathematical Imaging and Vision : Volume 33
Journal of Mathematical Imaging and Vision : Volume 32
Journal of Mathematical Imaging and Vision : Volume 31
Journal of Mathematical Imaging and Vision : Volume 30
Journal of Mathematical Imaging and Vision : Volume 29
Journal of Mathematical Imaging and Vision : Volume 28
Journal of Mathematical Imaging and Vision : Volume 27
Journal of Mathematical Imaging and Vision : Volume 26
Journal of Mathematical Imaging and Vision : Volume 25
Journal of Mathematical Imaging and Vision : Volume 24
Journal of Mathematical Imaging and Vision : Volume 23
Journal of Mathematical Imaging and Vision : Volume 22
Journal of Mathematical Imaging and Vision : Volume 21
Journal of Mathematical Imaging and Vision : Volume 20
Journal of Mathematical Imaging and Vision : Volume 19
Journal of Mathematical Imaging and Vision : Volume 18
Journal of Mathematical Imaging and Vision : Volume 17
Journal of Mathematical Imaging and Vision : Volume 16
Journal of Mathematical Imaging and Vision : Volume 15
Journal of Mathematical Imaging and Vision : Volume 14
Journal of Mathematical Imaging and Vision : Volume 13
Journal of Mathematical Imaging and Vision : Volume 12
Journal of Mathematical Imaging and Vision : Volume 11
Journal of Mathematical Imaging and Vision : Volume 10
Journal of Mathematical Imaging and Vision : Volume 9
Journal of Mathematical Imaging and Vision : Volume 8
Journal of Mathematical Imaging and Vision : Volume 7

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Rank Estimation in Missing Data Matrix Problems

Content Provider Springer Nature Link
Author Julià, Carme Sappa, Angel D. Lumbreras, Felipe Serrat, Joan López, Antonio
Copyright Year 2010
Abstract A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.
Starting Page 140
Ending Page 160
Page Count 21
File Format PDF
ISSN 09249907
Journal Journal of Mathematical Imaging and Vision
Volume Number 39
Issue Number 2
e-ISSN 15737683
Language English
Publisher Springer US
Publisher Date 2010-10-23
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Rank estimation Missing data Matrix completion techniques Control , Robotics, Mechatronics Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Artificial Intelligence (incl. Robotics)
Content Type Text
Resource Type Article
Subject Geometry and Topology Applied Mathematics Statistics and Probability Condensed Matter Physics Modeling and Simulation Computer Vision and Pattern Recognition
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