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<p>An algorithm is presented that is based on the regression updating theory for fitting linearly parameterizable curves without prior classification of edge data. An initial estimate of a hypothesized curve is first obtained by a statistical windowing technique. A search region is determined and iteratively grown. Edge points in the search region are tested for their goodness-of-fit to the previous estimate. The estimate is iteratively updated with the edge points that have favorable goodness-of-fit measures. The edge points having poor goodness-of-fit measures are rejected as outliers. The algorithm drives the estimate to converge to a final solution. By repeatedly applying this procedure to the edge data excluded from the previous fitting, all the underlying curves are reconstructed. The major feature that distinguishes this approach from that of others is that classifying the edge data prior to fitting is not required. Advantages of this algorithm are: (1) the fitting procedure achieves higher robustness and accuracy by dynamically analyzing the data consistency; (2) the computational complexity increases only linearly with the number of edge data; (3) the algorithm readily extends to reconstruct surfaces from range data. Thus the algorithm provides a powerful technique enabling a data-driven intermediate-level vision module to extract parametric features needed for higher-level processing.</p>
computer vision; curve fitting; iterative methods; data-driven intermediate level feature extraction algorithm; regression updating theory; hypothesized curve; statistical windowing technique; goodness-of-fit; edge points; computer vision; curve fitting; iterative methods

D. Chen, "A Data-Driven Intermediate Level Feature Extraction Algorithm," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 749-758, 1989.
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