Title :
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
fDate :
5/1/2007 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.1028
