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Scene-layout Compatible Conditional Random Field for Classifying Terrestrial Laser Point Clouds : Volume Ii-3, Issue 1 (07/08/2014)

By Luo, C.

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Book Id: WPLBN0003980043
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Scene-layout Compatible Conditional Random Field for Classifying Terrestrial Laser Point Clouds : Volume Ii-3, Issue 1 (07/08/2014)  
Author: Luo, C.
Volume: Vol. II-3, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Sohn, G., & Luo, C. (2014). Scene-layout Compatible Conditional Random Field for Classifying Terrestrial Laser Point Clouds : Volume Ii-3, Issue 1 (07/08/2014). Retrieved from http://worldebooklibrary.com/


Description
Description: GeoICT Laboratory, Department of Earth and Space Science, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada. Terrestrial Laser Scanning (TLS) rapidly becomes a primary surveying tool due to its fast acquisition of highly dense threedimensional point clouds. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. Conditional Random Field (CRF) is a well-known discriminative classifier, which integrates local appearance of the observation (laser point) with spatial interactions among its neighbouring points in classification process. Typical CRFs employ generic label consistency using short-range dependency only, which often causes locality problem. In this paper, we present a multi-range and asymmetric Conditional Random Field (CRF) (maCRF), which adopts a priori information of scene-layout compatibility addressing long-range dependency. The proposed CRF constructs two graphical models, one for enhancing a local labelling smoothness within short-range (srCRF) and the other for favouring a global and asymmetric regularity of spatial arrangement between different object classes within long-range (lrCRF). This maCRF classifier assumes two graphical models (srCRF and lrCRF) are independent of each other. Final labelling decision was accomplished by probabilistically combining prediction results obtained from two CRF models. We validated maCRF's performance with TLS point clouds acquired from RIEGL LMS-Z390i scanner using cross validation. Experiment results demonstrate that synergetic classification improvement can be achievable by incorporating two CRF models.

Summary
Scene-Layout Compatible Conditional Random Field for Classifying Terrestrial Laser Point Clouds

 

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