Specifications

puts forward a new interpolation method for lidar data visualization based on cubic spline
function. Firstly, the discrete characteristics of the lidar data are analyzed, and the key factors
that influence lidar data visualization graphics are determined. Secondly, based on the mutual
influence of the observation data in a certain atmosphere range, the interpolated data are
revised according to the observation data around the interpolation points, and the varying
tendency of lidar data is fitted with cubic spline function. The experimental results show that the
proposed method can improve the interpolation accuracy of discrete lidar data and the
smoothness of visualization graphics, which provides a strong means for lidar data analysis.
Number of references: 16
Main heading: Data visualization
Controlled terms: Atmospheric turbulence - Interpolation - Optical radar -
Visualization
Uncontrolled terms: Atmospheric parameters - Cubic spline functions - Data
interpolation - Data revision - Interpolation method - Interpolation points - LIDAR
data - Visualization software
Classification code: 443.1 Atmospheric Properties - 716.2 Radar Systems and Equipment -
902.1 Engineering Graphics - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set
Theory - 921.6 Numerical Methods
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
3.
Accession number: 20132216374185
Title: Multi-feature structure fusion of contours for unsupervised shape classification
Authors: Lin, Guangfeng1 ; Zhu, Hong2 ; Kang, Xiaobing1 ; Fan, Caixia1 ; Zhang, Erhu1/蔺广逢;
朱虹;;范彩霞;张二虎
Author affiliation: 1 Department of Information Science, Xi'An University of Technology, 5
South Jinhua Road, Xi'an, Xi'an Shaanxi Province 710048, China
2 Faculty of Automation and Information Engineering, Xi'An University of Technology, 5 South
Jinhua Road, Xi'an, Xi'an Shaanxi Province 710048, China
Corresponding author: Lin, G. ([e-mail])
Source title: Pattern Recognition Letters
Abbreviated source title: Pattern Recogn. Lett.
Volume: 34
Issue: 11
Issue date: 2013
Publication year: 2013
Pages: 1286-1290
Language: English
ISSN: 01678655
Document type: Journal article (JA)
Publisher: Elsevier, P.O. Box 211, Amsterdam, 1000 AE, Netherlands
Abstract: Nonlinear distortion, especially structure distortion, is one of the main reasons for the
poor performance of shape contour classification. The structure fusion of multiple features