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Type: Conference paper
Title: Maximum Kernel Density Estimator for Robust Fitting
Author: Wang, H.
Citation: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2008 : pp.3385-3388
Publisher: IEEE
Publisher Place: Online
Issue Date: 2008
Conference Name: International Conference on Acoustics, Speech and Signal Processing (2008 : Las Vegas, USA)
Statement of
Hanzi Wang
Abstract: Robust model fitting plays an important role in many computer vision applications. In this paper, we propose a new robust estimator — Maximum Kernel Density Estimator (MKDE) based on the nonparametric kernel density estimation technique. It can be viewed as an improved version of our previously proposed Quick Maximum Density Power Estimator (QMDPE) [15]. Compared with QMDPE, MKDE does not require running the mean shift algorithm for each candidate fit. Thus, the computational complexity of MKDE is greatly reduced while the accuracy of MKDE is comparable to QMDPE and outperforms that of other popular robust estimators such as LMedS and RANSAC. We evaluate MKDE in robust line fitting and fundamental matrix estimation. Experiments show that MKDE has achieved promising results.
Keywords: machine vision; robustness; modelfitting; kernel density estimation; algorithms
RMID: 0020093993
DOI: 10.1109/ICASSP.2008.4518377
Appears in Collections:Computer Science publications

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