Research of Discrete Curvature Estimation Method Based on Local Space Parabola

2014 ◽  
Vol 577 ◽  
pp. 802-805 ◽  
Author(s):  
Jian Wei Ma ◽  
Zhen Yuan Jia ◽  
Fu Ji Wang
Keyword(s):  
Point Cloud ◽  
Simulation Experiment ◽  
Estimation Method ◽  
Circular Arc ◽  
Discrete Curvature ◽  
Fitting Method ◽  
Local Space ◽  
Scan Line ◽  
Curvature Estimation ◽  
Line Point

Curvature estimation of 3-dimension discrete points performs an important role in dealing with scan line point cloud and is difficult to calculate. A discrete curvature estimation method based on local space parabola is proposed. Method in this paper is contrasted with circular arc fitting method and simulation experiment shows that the proposed method is feasible and effective with high precision.

CIRCULAR ARC SEGMENTATION BY CURVATURE ESTIMATION AND GEOMETRIC VALIDATION

2012 ◽  
Vol 12 (04) ◽  
pp. 1250024 ◽  
Author(s):  
SHYAMOSREE PAL ◽  
RAHUL DUTTA ◽  
PARTHA BHOWMICK
Keyword(s):  
Geometric Analysis ◽  
Circular Arc ◽  
Discrete Curvature ◽  
Circular Arcs ◽  
Initial Stage ◽  
Benchmark Datasets ◽  
Curvature Estimation ◽  
Digital Curve ◽  
Curvature Information ◽  
Novel Algorithm

A novel algorithm to detect circular arcs from a digital image is proposed. The algorithm is based on discrete curvature estimated for the constituent points of digital curve segments, followed by a fast geometric analysis. The curvature information is used in the initial stage to find the potentially circular segments. In the final stage, the circular arcs are merged and maximized in length using the radius and center information of the potentially circular segments. Triplets of longer segments are given higher priorities; doublets and singleton arcs are processed at the end. Detailed experimental results on benchmark datasets demonstrate its efficiency and robustness.

A New Method of Data Smoothing for Scan-Line Point Cloud in Reverse Engineering

2014 ◽  
Vol 1006-1007 ◽  
pp. 352-355
Author(s):  
Ping Zhao ◽  
Xue Wei Bai ◽  
Yong Kui Li ◽  
Ying Li ◽  
Chang Yi Lv
Keyword(s):  
Point Cloud ◽  
Median Filter ◽  
Research Result ◽  
Original Data ◽  
Filter Method ◽  
Data Smoothing ◽  
Cloud Data ◽  
Mean Filter ◽  
Scan Line ◽  
Line Point

Based on summarizing characteristics of three common kinds of filter method (median filter, mean filter and Gaussian filter) through theory analysis and experiments in Imageware software, a more effective method of smoothing point cloud data was proposed, and the algorithm was realized by programming in MATLAB. Experimental results show that the proposed method combines the advantages of the median filter and mean filter in together, achieves the purpose of smoothing, also ensures the shape of the original data. This research result will lay a foundation for subsequent reconstruction precision of curved surface.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

2016 ◽  
Vol 13 (12) ◽  
pp. 1842-1846 ◽  
Author(s):  
Zhizhong Kang ◽  
Ruofei Zhong ◽  
Ai Wu ◽  
Zhenwei Shi ◽  
Zhongfei Luo
Keyword(s):  
Point Cloud ◽  
Fitting Method ◽  
Feature Fitting ◽  
Point Cloud Simplification ◽  
Planar Feature

scholarly journals The Parameter Estimation of Double Exponential Pulse Based on a Least Infinity Norm Fitting Method

2021 ◽  
Vol 2083 (4) ◽  
pp. 042002
Author(s):  
Yuewu Shi ◽  
Wei Wang ◽  
Zhizhen Zhu ◽  
Xin Nie
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Maximum Amplitude ◽  
Least Square Method ◽  
Least Square ◽  
Physical Parameters ◽  
Infinity Norm ◽  
Fitting Method ◽  
Double Exponential ◽  
Exponential Pulse

Abstract This paper presents an estimation method of double exponential pulse (DEP) between the physical parameters rise time (t r), full width at half maximum amplitude (t FWHM) and the mathematical parameters α, β. A newly fitting method based on the least infinity norm criterion is proposed to deal with the estimation problem of DEP. The calculation process and equation of parameters of this method is proposed based on an m-th-order polynomial fitting model. This estimation method is compared with the least square method by the same data and fitting function. The results show that the maximum estimation error of parameters of double exponential pulse obtained by the least infinity norm method is 1.5 %.

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