Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers

Abstract—We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiplestructure 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.

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Hanzi Wang, Tat-Jun Chin, David Suter
• Keywords: Motion Segmentation, PAMI 2012, Range Image Segmentation, Robust Estimators, Software Engineering