Using Structural Risk Minimization to Determine the Optimal Complexity of B-Spline Surfaces for Modelling Correlated Point Cloud Data

2020 ◽  
pp. 165-174
Author(s):  
Corinna Harmening ◽  
Hans Neuner
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Risk Minimization ◽  
Structural Risk Minimization ◽  
Optimal Complexity ◽  
B Spline ◽  
Cloud Data ◽  
Spline Surfaces ◽  
Structural Risk

Highly Anti-Noise and Adaptive B-Spline Curve Reconstruction Based on ICT Slice Images

2013 ◽  
Vol 397-400 ◽  
pp. 1083-1087
Author(s):  
Guang Shuai Liu ◽  
Bai Lin Li
Keyword(s):  
Point Cloud ◽  
Adaptive Method ◽  
Point Cloud Data ◽  
Curve Reconstruction ◽  
B Spline ◽  
Cloud Data ◽  
Spline Curve ◽  
Multi Scale ◽  
Key Issues ◽  
Selection Of

Obtaining effective value points is one of key issues in cubic B-spline curve reconstruction. Since it is unfavorable for the selection of value points through curvature methods and the point cloud data acquired from ICT slice images is characterized with large volume of data, high noise and density, a baseline adaptive method is presented to get value points for curve reconstruction, baseline and scale threshold determined by wavelet multi-scale, in which the value points is obtained and curve is reconstructed automatically. Hausdorff distance is adopted to calculate the error of cubic B-spline curve reconstruction. Comparative analysis with existing methods proves that our method can effectively restrain noise and quickly reconstruct contour curves.

scholarly journals Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks

2010 ◽  
Vol 20 (3) ◽  
pp. 525-544
Author(s):  
Przemysław Klęsk
Keyword(s):  
Error Estimate ◽  
Sample Size ◽  
Cross Validation ◽  
Generalization Error ◽  
Risk Minimization ◽  
Structural Risk Minimization ◽  
Optimal Complexity ◽  
Structural Risk ◽  
Fold Cross Validation

Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risksTwo known approaches to complexity selection are taken under consideration:n-fold cross-validation and structural risk minimization. Obviously, in either approach, a discrepancy between the indicated optimal complexity (indicated as the minimum of a generalization error estimate or a bound) and the genuine minimum ofunknown true risksis possible. In the paper, this problem is posed in a novel quantitative way. We state and prove theorems demonstrating how one can calculate pessimistic probabilities of discrepancy between these minima for given for given conditions of an experiment. The probabilities are calculated in terms of all relevant constants: the sample size, the number of cross-validation folds, the capacity of the set of approximating functions and bounds on this set. We report experiments carried out to validate the results.

Study on Sectional Curve Fitting in Reverse Modeling of Mechanical Parts

2012 ◽  
Vol 197 ◽  
pp. 68-72
Author(s):  
Qun Zhang Tu ◽  
Jian Xun Zhao ◽  
Long Qin ◽  
Jvying Dai
Keyword(s):  
Curve Fitting ◽  
Point Cloud ◽  
Torque Converter ◽  
Point Cloud Data ◽  
B Spline ◽  
Cloud Data ◽  
Mechanical Parts ◽  
Stator Vane ◽  
Hydraulic Torque Converter ◽  
Fitting In

The flow of reverse modeling based on section feature is analyzed, and three algorithms of B-spline curve fitting are studied. Then by adopting the three methods, the sectional curve fitting of the point cloud data is achieved for the stator vane of hydraulic torque converter. Through comparing the errors and curvature of the fitting curves, the effect of curve fitting is analyzed and valuable conclusions are obtained.

scholarly journals Analysis of point cloud data of tunnel cross-section using cubic B-spline curve

2021 ◽  
Vol 643 ◽  
pp. 012064
Author(s):  
Zhiyuan Li ◽  
Jian Wang ◽  
Fengqi Zhu ◽  
Digui Li ◽  
Wanqi Gu
Keyword(s):  
Cross Section ◽  
Point Cloud ◽  
Point Cloud Data ◽  
B Spline ◽  
Cloud Data ◽  
Spline Curve ◽  
Tunnel Cross Section

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

REPRESENTATION AND MODELING OF THREE-DIMENSIONAL LANDFORM DATA BASED ON POINT CLOUD DATA USING VARIOUS MEASUREMENT HARDWARE

2018 ◽  
Vol 74 (2) ◽  
pp. II_99-II_109 ◽  
Author(s):  
Satoshi KUBOTA ◽  
Ryuichi IMAI ◽  
Kenji NAKAMURA ◽  
Jun SAKURAI ◽  
Shigenori TANAKA
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Cloud Data

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data
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