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A non-parametric inference technique for shape boundaries in noisy point clouds | IEEE Conference Publication | IEEE Xplore

A non-parametric inference technique for shape boundaries in noisy point clouds


Abstract:

This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape informati...Show More

Abstract:

This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape information is not known, the offered algorithm estimates points on the shape boundary by using the statistics of the subsets of point cloud data. The novel approach proposed in this paper is able to find corner points in a local geometry by only using sample mean and covariance matrices of the subsets of the point cloud. While the proposed approach can be used for any class of boundary functions that demonstrates symmetry; for this paper, the analysis and experiments are performed on a connected line segment.
Date of Conference: 16-18 November 2017
Date Added to IEEE Xplore: 11 December 2017
ISBN Information:
Conference Location: Daegu, Korea (South)

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