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ECCV
2004
Springer

A Robust Algorithm for Characterizing Anisotropic Local Structures

10 years 29 days ago
A Robust Algorithm for Characterizing Anisotropic Local Structures
This paper proposes a robust estimation and validation framework for characterizing local structures in a positive multi-variate continuous function approximated by a Gaussian-based model. The new solution is robust against data with large deviations from the model and margin-truncations induced by neighboring structures. To this goal, it unifies robust statistical estimation for parametric model fitting and multi-scale analysis based on continuous scale-space theory. The unification is realized by formally extending the mean shift-based density analysis towards continuous signals whose local structure is characterized by an anisotropic fully-parameterized covariance matrix. A statistical validation method based on analyzing residual error of the chi-square fitting is also proposed to complement this estimation framework. The strength of our solution is the aforementioned robustness. Experiments with synthetic 1D and 2D data clearly demonstrate this advantage in comparison with the -no...
Kazunori Okada, Dorin Comaniciu, Navneet Dalal, Ar
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Kazunori Okada, Dorin Comaniciu, Navneet Dalal, Arun Krishnan
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