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Subspace procrustes analysis

Author
Perez-Sala, X.; De La Torre, F.; Igual, L.; Escalera, S.; Angulo, C.
Type of activity
Journal article
Journal
International journal of computer vision
Date of publication
2017-02
Volume
121
Number
3
First page
327
Last page
343
DOI
https://doi.org/10.1007/s11263-016-0938-x Open in new window
Project funding
PATRICIA. TIN2012-38416-C03-01
Repository
http://hdl.handle.net/2117/101519 Open in new window
URL
http://link.springer.com/article/10.1007%2Fs11263-016-0938-x Open in new window
Abstract
Procrustes analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Later, a non-rigid 2-D model is computed by modeling the residual (e.g., PCA). Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3...
Citation
Perez-Sala, X., De La Torre, F., Igual, L., Escalera, S., Angulo, C. Subspace procrustes analysis. "International journal of computer vision", Febrer 2017, vol. 121, núm. 3, p. 327-343.
Keywords
Functional subspace learning, Learning 2D shape models, Procrustes analysis
Group of research
GREC - Knowledge Engineering Research Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

  • Perez Sala, Xavier  (author)
  • De La Torre, Fernando  (author)
  • Igual, Laura  (author)
  • Escalera, Sergio  (author)
  • Angulo Bahón, Cecilio  (author)

Attachments