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

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 unde...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2007-05, Vol.29 (5), p.901-905
Main Authors: Vik, T., Heitz, F., Charbonnier, P.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Computer Science
Computer science
control theory
systems
Context modeling
Exact sciences and technology
Gaussian
Gaussian distribution
half-quadratic theory
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image Processing
Image recognition
Image representation
Imaging, Three-Dimensional - methods
Inference algorithms
Information Storage and Retrieval - methods
Intelligence
Maximum likelihood estimation
mean shift
Models, Statistical
Noise robustness
Non-Gaussian
nonparametric statistics
Normal Distribution
Object recognition
Pattern analysis
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Principal component analysis
probabilistic PCA
Recognition
Reproducibility of Results
robust regression
Sensitivity and Specificity
Statistical distributions
Statistical image representation
Subspaces
Visual
visual appearance
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Description
Summary: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
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2007.1028