Robust Pose Estimation and Recognition Using Non-Gaussian Modeling of Appearance Subspaces

Author

Vik, Torbjørn ; Heitz, Fabrice ; Charbonnier, Pierre

Author_Institution

Phillips Res. Eur., Hamburg

Volume

29

Issue

5

fYear

2007

fDate

5/1/2007 12:00:00 AM

Firstpage

901

Lastpage

905

Abstract

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm

Keywords

Gaussian processes; object recognition; pose estimation; Gaussian visual subspace model; mean shift algorithm; nonGaussian modeling; nonparametric distributions; pose recognition; robust pose estimation; Context modeling; Gaussian distribution; Image recognition; Image representation; Inference algorithms; Maximum likelihood estimation; Noise robustness; Object recognition; Principal component analysis; Statistical distributions; Statistical image representation; half-quadratic theory.; mean shift; nonparametric statistics; object recognition; probabilistic PCA; robust regression; visual appearance; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Models, Statistical; Normal Distribution; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2007.1028

Filename

4135684