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A STUDY OF FEATURE POINTS EXTRACTION BASED ON POINT CLOUD DATA SETS AND MODEL SIMPLIFICATION IN GOAF
Feature Points Extraction 3D laser scanner Point Cloud Data Sets Model Simplification Goaf
2016/3/17
The shape measurement of goaf is an important job in mine project. With the rapid development of the measuring equipment, 3D laser scanner, which makes the efficiency of traditional work being improve...
BUILDING RECONSTRUCTION BASED ON MDL PRINCIPLE FROM 3-D FEATURE POINTS
Reconstruction Urban Three-dimensional Method Building
2015/3/24
For 3-D building reconstructions of urban areas, we present a fully automatic shape recovery method that uses 3-D points acquired
from aerial image sequences. This paper focuses on shape recovery of...
SHAPE RECOVERY FROM HYBRID FEATURE POINTS WITH FACTORIZATION METHOD
Factorization Method Hybrid Feature Points Shape from Motion 3D Digital City
2014/12/31
We are developing shape recovery technology that can semi-automatically process image sequences using the idea of ”Shape from Motion”. In this paper, we investigate an acquisition and recovery method ...
Study on Technology for Precision Correction of Land Survey Data based on Plotting Result Data and Feature Points
Land Use Correction Model Orthoimage Mapping Accuracy Geometric High resolution
2014/7/24
High‐precision land survey data is important foundation for fine management of land resources and enhancement of land use efficiency. The main method for land survey is remote sensing (RS) survey. How...
A MULTI-VIEW IMAGE MATCHING METHOD FOR FEATURE POINTS BASED ON THE MOVING Z-PLANE CONSTRAINT
Multi-View Image Matching Feature Points Matching Moving Z-Plane Constraint Grid Cell Occlusion
2014/4/28
Focusing on the serious occlusion problem in city images, this paper makes full use of the advantage of multi-view image matching, and proposes a reliable multi-view image matching method based on the...
叶片类截面数据特征点精确识别方法（Accurate Recognition Method for Cross-section Data Feature Points of Blades）
叶片 特征识别 小波模极大值
2010/1/28
利用小波模极大值方法可很好地对特征进行识别，并能抑制噪声的影响。利用该方法对叶片截面数据进行特征识别时，某些重要的局部特征在细尺度下会消失，导致数据特征点不能完整识别，针对该情况提出特征尺度因子的概念，以了解数据中所含特征的差异性。特征尺度因子越大意味着可分解尺度数越多，相反则意味着可分解尺度数越少，当数据点的特征差异较大时，可将其分为几段分别进行识别，最后再将各段特征点汇总。实验证明，特征尺度因...