Generalized kernel density estimation based robust estimator and its application

Authors:
Zhen Zhang;Yanning Zhang;Rui Yao;Haisen Li;Yu Zhu
Affiliations:
Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, The P.R.C.;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, The P.R.C.;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, The P.R.C.;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, The P.R.C.;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, The P.R.C.
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

In this paper, a new General Kernel Density Estimator (GKDE) based robust estimator is presented. The GKDE based robust estimator utilizes GKDE to estimate the distribution of data points and by using local adaptive bandwidth estimator, the scale of inliers or user-specified error threshold is not need. Compared to ASKC, pbM and other Kernel Density Estimation based robust estimator which do not have locality, GKDE has higher resolution for inliers, and experiments show that it has higher precision than traditional robust estimator such as RANSAC, LMeds. We also applied GKDE based estimator to image mosaic for homography estimation.