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Type: Journal article
Title: Simultaneously fitting and segmenting multiple-structure data with outliers
Author: Wang, H.
Chin, T.
Suter, D.
Citation: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012; 34(6):1177-1192
Publisher: IEEE Computer Soc
Issue Date: 2012
ISSN: 0162-8828
Statement of
Hanzi Wang, Tat-Jun Chin and David Suter
Abstract: We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Ordered Scale Estimator (IKOSE). IKOSE can accurately estimate the scale of inliers for heavily corrupted multiplestructure data and is of interest by itself since it can be used in other robust estimators. In addition to IKOSE, our framework includes several original elements based on the weighting, clustering, and fusing of hypotheses. AKSWH can provide accurate estimates of the number of model instances and the parameters and the scale of each model instance simultaneously. We demonstrate good performance in practical applications such as line fitting, circle fitting, range image segmentation, homography estimation, and two-view-based motion segmentation, using both synthetic data and real images.
Keywords: Robust statistics
model fitting
scale estimation
kernel density estimation
multiple structure segmentation
Rights: © 2012 IEEE
DOI: 10.1109/TPAMI.2011.216
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