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.
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Zhang, Z., Zhang, Y., Yao, R., Li, H., Zhu, Y. (2012). Generalized Kernel Density Estimation Based Robust Estimator and Its Application. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_15
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DOI: https://doi.org/10.1007/978-3-642-31919-8_15
Publisher Name: Springer, Berlin, Heidelberg
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