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Feature Relevance Assessment for the Semantic Interpretation of 3D Point Cloud Data : Volume Ii-5/W2, Issue 1 (16/10/2013)

By Weinmann, M.

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Book Id: WPLBN0004014139
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Feature Relevance Assessment for the Semantic Interpretation of 3D Point Cloud Data : Volume Ii-5/W2, Issue 1 (16/10/2013)  
Author: Weinmann, M.
Volume: Vol. II-5/W2, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Mallet, C., Weinmann, M., & Jutzi, B. (2013). Feature Relevance Assessment for the Semantic Interpretation of 3D Point Cloud Data : Volume Ii-5/W2, Issue 1 (16/10/2013). Retrieved from http://www.ebooklibrary.org/


Description
Description: Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany. The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

Summary
Feature relevance assessment for the semantic interpretation of 3D point cloud data

 

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