Missing data estimation using polynomial kernels

Maxime Berar (1) , Michel Desvignes (1) , Gérard Bailly (2) , Yohan Payan (3)

1 LIS - Laboratoire des images et des signaux
2 ICP - Institut de la communication parlée
3 TIMC-IMAG-GMCAO - Gestes Medico-chirurgicaux Assistés par Ordinateur

In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statistical model. We presents two model in this paper : a linear model based on PCA and a non-linear model based on KPCA. The present work attempts to localize of non visible parts of an object, from the visible part and from the model, using the variability represented by the models. Both are applied to synthesis data and to cephalometric data with good results.

Mots clés: PCA; KPCA; statistical models; Image; Pattern recognition

Domaines: Biomécanique [physics.med-ph]; Biomécanique [physics.med-ph]; Statistiques [math.ST]

Maxime Berar, Michel Desvignes, Gérard Bailly, Yohan Payan. Missing data estimation using polynomial kernels. Lecture Notes in Computer Science, 2005, 3686, pp.390-399. ⟨hal-00081903⟩