  The Progressive-X algorithm, Prog-X in short, is proposed for geometric
multi-model fitting. The method interleaves sampling and consolidation of the
current data interpretation via repetitive hypothesis proposal, fast rejection,
and integration of the new hypothesis into the kept instance set by labeling
energy minimization. Due to exploring the data progressively, the method has
several beneficial properties compared with the state-of-the-art. First, a
clear criterion, adopted from RANSAC, controls the termination and stops the
algorithm when the probability of finding a new model with a reasonable number
of inliers falls below a threshold. Second, Prog-X is an any-time algorithm.
Thus, whenever is interrupted, e.g. due to a time limit, the returned instances
cover real and, likely, the most dominant ones. The method is superior to the
state-of-the-art in terms of accuracy in both synthetic experiments and on
publicly available real-world datasets for homography, two-view motion, and
motion segmentation.

Computer Vision and Pattern Recognition