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We present a novel algorithm for generating robust and consistent hypothesesfor multiple-structure model fitting. Most of the existing methods utilizerandom sampling which produce varying results especially when outlier ratio ishigh. For a structure where a model is fitted, the inliers of other structuresare regarded as outliers when multiple structures are present. Globaloptimization has recently been investigated to provide stable and uniquesolutions, but the computational cost of the algorithms is prohibitively highfor most image data with reasonable sizes. The algorithm presented in thispaper uses a maximum feasible subsystem (MaxFS) algorithm to generateconsistent initial hypotheses only from partial datasets in spatiallyoverlapping local image regions. Our assumption is that each genuine structurewill exist as a dominant structure in at least one of the local regions. Torefine initial hypotheses estimated from partial datasets and to removeresidual tolerance dependency of the MaxFS algorithm, iterative re-weighted L1(IRL1) minimization is performed for all the image data. Initial weights ofIRL1 framework are determined from the initial hypotheses generated in localregions. Our approach is significantly more efficient than those that use onlyglobal optimization for all the image data. Experimental results demonstratethat the presented method can generate more reliable and consistent hypothesesthan random-sampling methods for estimating single and multiple structures fromdata with a large amount of outliers. We clearly expose the influence ofalgorithm parameter settings on the results in our experiments.

Deterministic Hypothesis Generation for Robust Fitting of Multiple Structures