D3 Article in a professional conference publication
Heuristicallly targeted Minimum Description Length test for stone detection from public point cloud data
Authors: Paavo Nevalainen , Juuso Suomi , Jukka Heikkonen
Editors: Rissanen Jorma, Leppä-Aho Janne, Roos Teemu, Myllymäki Petri
Conference name: Workshop on Information Theoretic Methods in Science and Engineering
Publishing place: Helsinki
Publication year: 2016
Book title : Proceedings of the Ninth Workshop on Information Theoretic Methods in Science and Engineering
Series title: Publication series B, Report B
Number in series: 9
Volume: 1
First page : 26
Last page: 29
Web address : https://helda.helsinki.fi/bitstream/handle/10138/169918/witmse2016proc.pdf?sequence=3
Coarse cross-terrain point clouds are gathered by aerial
laser scan (ALS) and dense point clouds by unmanned
vehicle (UAV) operation. These two data sources have
complementary nature and should be combined for various
applications. This paper uses minimum description
(MDL) length approach to detect individual stones and
their physical dimensions from UAV data. The MDL procedure
is spatially targeted by a two-step heuristics: local
stoniness likelihood derived from ALS data and the curvature
detection on UAV data. Comparison of the performance
of MDL principle and a geometric approach,
namely mean square error (MSE) minimization is presented.
The MDL approach can be applied to cloud point
densities ρ ≥ 3 m−2.
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