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A Generalized Kernel Consensus-Based Robust Estimator
January 2010 (vol. 32 no. 1)
pp. 178-184
ASCII Text
x 
 
Hanzi Wang, Daniel Mirota, Gregory D. Hager, "A Generalized Kernel Consensus-Based Robust Estimator," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 178-184, January, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.148,
author = {Hanzi Wang and Daniel Mirota and Gregory D. Hager},
title = {A Generalized Kernel Consensus-Based Robust Estimator},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {1},
issn = {0162-8828},
year = {2010},
pages = {178-184},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.148},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Generalized Kernel Consensus-Based Robust Estimator
IS - 1
SN - 0162-8828
SP178
EP184
EPD - 178-184
A1 - Hanzi Wang,
A1 - Daniel Mirota,
A1 - Gregory D. Hager,
PY - 2010
KW - Robust statistics
KW - model fitting
KW - kernel density estimation
KW - motion estimation
KW - pose estimation.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Hanzi Wang, The University of Adelaide, Adelaide
Daniel Mirota, The Johns Hopkins University, Baltimore
Gregory D. Hager, The Johns Hopkins University, Baltimore
In this paper, we present a new Adaptive-Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANdom SAmple Consensus (RANSAC), Adaptive Scale Sample Consensus (ASSC), and Maximum Kernel Density Estimator (MKDE). The ASKC framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.

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Index Terms:
Robust statistics, model fitting, kernel density estimation, motion estimation, pose estimation.
Citation:
Hanzi Wang, Daniel Mirota, Gregory D. Hager, "A Generalized Kernel Consensus-Based Robust Estimator," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 178-184, Jan. 2010, doi:10.1109/TPAMI.2009.148
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