Segmentation of Laser Point Clouds in Urban Areas by a Modified Normalized Cut Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Avishek DuttaMichael Hahn

Abstract

Normalized Cut is a well-established divisive image segmentation method, which we adapt in this paper for the segmentation of laser point clouds in urban areas. Our focus is on polyhedral objects with planar surfaces. Due to its target function, Normalized Cut favours cuts with "short cut lines" or "small cut surfaces", which is a drawback for our application. We therefore modify the target function, weighting the similarity measures with distance-dependent weights. We call the induced minimization problem "Distance-weighted Cut" (DWCut). The new target function leads to a generalized eigenvalue problem, which is slightly more complicated than the corresponding problem for the Normalized Cut; on the other hand, the new target function is easier to interpret and avoids some drawbacks of the Normalized Cut. We point out an efficient method for the numerical solution of the eigenvalue problem which is based on a Krylov subspace method. DWCut can be beneficially combined with an aggregation in order to reduce the computational effort and to avoid shortcomings due to insufficient plane parameters.

Citations

Jul 22, 2020·PLoS Computational Biology·Adrien DoerigMichael H Herzog
Dec 14, 2019·Neural Computation·Xiaofeng XuChuancai Liu

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