Issue No. 06 - June (2012 vol. 34)
David Suter , The University of Adelaide, Adelaide
Tat-Jun Chin , The University of Adelaide, Adelaide
Hanzi Wang , Xiamen University, Xiamen, and The University of Adelaide, North Terrace
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 multiple-structure 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.
Robust statistics, model fitting, scale estimation, kernel density estimation, multiple structure segmentation.
David Suter, Tat-Jun Chin, Hanzi Wang, "Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1177-1192, June 2012, doi:10.1109/TPAMI.2011.216