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...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Kazunori Okada, Dorin Comaniciu, Navneet Dalal, Arun Krishnan
• Keywords: Computer Vision, ECCV 2004, Estimation Framework, Multi-variate Continuous Function, Robust Estimation, Robust Statistical Estimation, Sample Estimation Approach