Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods.     

中国地区电离层foF2重构方法及其在短期预报中的应用

提出了一种适合于中国地区电离层f_oF₂的重构方法——以中国参考电离层为背景的改进克里格法.该方法把f_oF₂的估计值与中国参考电离层模型值之差值的相对值作为区域化变量,引入电离层距离,采用克里格法实现区域电离层重构.与直接利用f_oF₂进行克里格重构相比,以中国参考电离层为背景场保持了电离层的区域特征,提高了重构的准确性和稳定性.利用我国电离层垂测台站网的数据对该方法应用于中国地区的重构精度进行了评估.该方法与单站预报的自相关分析法相结合可实现中国地区电离层f_oF₂短期预报.     

显式分形插值在有限元叠前逆时偏移成像中的应用

以分形理论为基础,借助于压缩映射原理和不动点理论及实变函数理论,给出了分形插值函数的显式表达形式,同时给出了垂直比例因子的局部显式表达式.利用该显式分形插值方法对稀疏采样的地震记录进行了插值加密处理,并对原始记录和分形插值重建记录进行了叠前逆时偏移处理.数值重建实验表明,显式分形插值重建的剖面与原始地震剖面非常类似,单道地震记录则表明显式分形插值重建的地震道是原始地震道的良好近似,缺失道的振幅和相位都得到了很好的估计.叠前偏移结果则表明,分形插值叠前偏移剖面是原始记录偏移剖面的良好近似,二者的分辨率几乎一样.     

基于Bayes估计的稀疏数据CT重建算法研究

通过对稀疏放射数据CT重建问题的分析,提出用Bayes方法重建图像。首先以医疗解剖学为基础,将目标物体的先验信息转化为定量的先验概率密度,即结构先验,将测量所得数据作为似然函数,二者结合得到后验概率密度,通过对其抽样,将均值估算得到期望值作为重建结果。仿真实验证明,与传统CT重建方法相比,Bayes重建放射数据少,重建时间短,图像质量好,抗噪声能力强。     

洞穴碳酸盐微层研究及其发展方向

近几年,在分辨率气候－环境变化研究中,洞穴碳酸盐微层逐渐受到人们重视。洞穴碳酸盐微层的类型较多,主要为年层。微层是在沉积条件发生高频率的周期性改变时形成的,要受到供水条件和碳酸钙浓度等多因素的控制。洞穴碳酸盐微层的稳定同位素组成,生长速率,微层结构与物质组分,微层厚度与灰度和光学特征等可以反映降水,温度,土壤成分及植被变化等信息。     

SVD方法在信号重构中的应用

由时域有限连续信号的部分频段值重构其它频段值的问题,可以转化为求解线性方程Ａ·ｘ＝ｂ的问题。奇异值分解（ＳＶＤ）方法能够清楚地刻画算子Ａ的值域和数值零空间,通过计算信号与所张成值域的正交基的内积,可以预先判断信号重构性能的好坏。数值计算表明ＳＶＤＴｉｋｈｏｎｏｖ正则解比Ｐａｐｏｕｌｉｓ－Ｇｅｒｃｈｂｅｒｇ（ＰＧ）算法在色噪污染情况下有更好的抗干扰性。     

Development of a Speckle Interferometer and the Measurement of Fried's Parameter (ro) at the Telescope Site

A new optical speckle interferometer for use at the 2.34 meter Vainu Bappu Telescope (VBT), at Vainu Bappu Observatory (VBO), Kavalur, India, has been designed and developed. Provisions have been made for observation both at the prime focus (f/3,25), as well as at the Cassegrain focus (f/13) of the said telescope. The technical details of this sensitive instrument and the design features are described. An interface between the telescope and the afore-mentioned interferometer is made based on a concept of eliminating the formation of eddies due to the hot air entrapment. The performances of this instrument has been tested both at the laboratory, as well as at the Cassegrain end of the telescope. It is being used routinely to observe the speckle-grams of close-binary (separation < 1 arc second) stars. The size of the Fried's parameter, r_o, is also measured.     

Observations of Ebria tripartita (Schumann) Lemmermann in Baltic sediments

Sediment finds of Ebria tripartita (Schumann) Lemmermann in the Baltic Sea are reported and light and electron micrographs of the remains of the taxon are presented. Stratigraphic ebriid analysis performed on two sediment cores (one from the deep Gotland Basin and the other from the shallow Töölö bay, central Helsinki) provided clear indications of frequency changes of E. tripartita. These changes are apparently related to the variations in trophic status and salinity, yet competition and degree of preservation may also have contributed to the distributions. Due to identification problems, the species may actually be more common in the sediment material of the Baltic Sea than has hitherto been recognized. E. tripartita may have a significant potential as an indicator species in paleolimnological research as more information is gathered about its present-day ecological requirements.     

Splatting算法在三维CT迭代算法中的应用

采用代数重建技术重建三维物体,提出了一种基于splatting原理的ART权值计算方法,以增强重建图像的空间分辨率.该算法基于三维锥束扫描方式,采用双三次样条局部基函数作为插值核,并利用其足印函数对物体进行插值来计算像素的权值.与常量插值相比,这种方法对物体的灰度值有更精确的近似,在相同的实验条件下可重建出分辨率高的物体.给出了适用于三维锥束重建的splatting算法的具体实现,并对实验数据进行了实物重建及误差分析.     

统计重建算法综述

本文主要综述应用于PET/SPECT领域的统计重建算法,介绍统计重建算法发展的历史,最新的研究成果及研究方向,总结统计重建算法发展的三个概念:有序子集加速、增量方法和优化变换.对现有算法进行归纳总结,关注统计重建的发展带来变革的概念和方法,重点论述算法的思想,勾勒统计重建方法发展的主线.