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This paper introduces a probabilistic polygonal representation for plane curves using Gaussian Mixture Models, preserving local tangent, normal, and arc length while encoding uncertainty in the normal direction. The framework applies to various plane curves and supports uncertainty-aware geometric modeling for CAD, robotics, and trajectory planning.
This paper proposes a novel evidential information fusion framework based on a reversible transformation between belief functions and possibilistic structure, using triangular norms for flexible combination beyond Dempster's rule.