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<p>This correspondence presents a segmentation and fitting method using a new robust estimation technique. We present a robust estimation method with high breakdown point which can tolerate more than 80% of outliers. The method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type. From K samples, we choose one set of sample points that determines a best-fit equation for the largest homogeneous surface patch in the region. This choice is made by measuring a residual consensus (RESC), using a compressed histogram method which is effective at various noise levels. After we get the best-fit surface parameters, the surface patch can be segmented from the region and the process is repeated until no pixel left. The method segments the range image into planar and quadratic surfaces. The RESC method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus. A genetic algorithm is also incorporated to accelerate the random search.</p>
image segmentation; image reconstruction; genetic algorithms; robust estimation; range image segmentation; image reconstruction; primitive parameters; homogeneous surface patch; residual consensus; RESC; compressed histogram method; best-fit surface parameters; planar surfaces; quadratic surfaces; least median squares method; genetic algorithm; random search

A. Krzyzak, X. Yu and T. Bui, "Robust Estimation for Range Image Segmentation and Reconstruction," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 530-538, 1994.
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