Output details
11 - Computer Science and Informatics
Oxford Brookes University
Article title
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
Type
D - Journal article
Title of journal
International Journal of Computer Vision
Article number
-
Volume number
79
Issue number
1
First page of article
85
ISSN of journal
1573-1405
Year of publication
2008
URL
-
Number of additional authors
2
Additional information
<23> Identification of independently moving objects seen in a sequence of images is a hard problem that has been well studied. This problem is further complicated by missing data, where points cannot be successfully tracked through the whole
image sequence. This paper introduces a particularly simple and novel method for filling in missing data by a new algorithm known as PowerFactorization. Through simultaneous dimension reduction, the problem is made tractable
by simple linear techniques. Since the PowerFactorization method is applicable to different problems, this paper is well cited by papers in many different areas.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
38
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
