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Surfaces in range image understanding
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Pattern Recognition
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
International Journal of Computer Vision
A consensus sampling technique for fast and robust model fitting
Pattern Recognition
Journal of Mathematical Imaging and Vision
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
Pattern Recognition Letters
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
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ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Adaptive-Scale robust estimator using distribution model fitting
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Efficient and robust model fitting with unknown noise scale
Image and Vision Computing
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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.