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排序方式: 共有249条查询结果,搜索用时 531 毫秒
1.
图像平滑算法比较研究及改进策略 总被引:9,自引:0,他引:9
图像平滑作为去除图像中含有噪声的图像增强处理技术,是各种与图像有关软件中必不可少的功能模块。文中在分析噪声模型的基础上,对均值平滑、中值平滑、边缘保持平滑等技术从图像处理空间域中的算法原理、实现方法及效率等方面进行了详细比较。针对图像中含有的不同噪声模型,通过多次试验,得出了值得从事图像处理研究者参考的结论。这将有助于提高图像处理软件开发的效率。 相似文献
2.
用L-曲线法确定半参数模型中的平滑因子 总被引:7,自引:0,他引:7
提出了一种新的方法——L-曲线法确定平滑因子。通过确定合适的平滑因子,更好地控制了残差部分V^TPV与光滑度部分S^TRS之间的平衡,得到了更准确的参数估值。通过算例,将基于L-曲线法确定平滑因子的半参数模型解算方法和其他方法进行了比较。结果表明,用L-曲线法确定平滑因子后,提高了半参数模型计算结果的精度,可以更好地将观测值中的系统误差分离出来。 相似文献
3.
Correcting the Smoothing Effect of Ordinary Kriging Estimates 总被引:2,自引:0,他引:2
Jorge Kazuo Yamamoto 《Mathematical Geology》2005,37(1):69-94
The smoothing effect of ordinary kriging is a well-known dangerous effect associated with this estimation technique. Consequently kriging estimates do not reproduce both histogram and semivariogram model of sample data. A four-step procedure for correcting the smoothing effect of ordinary kriging estimates is shown to be efficient for the reproduction of histogram and semivariogram without loss of local accuracy. Furthermore, this procedure provides a unique map sharing both local and global accuracies. Ordinary kriging with a proper correction for smoothing effect can be revitalized as a reliable estimation method that allows a better use of the available information. 相似文献
4.
5.
AEKF在星敏感器低频误差补偿中的应用 总被引:1,自引:0,他引:1
高分辨率对地观测卫星需要精确的姿态信息来满足后续对地定位等工作,因此姿态确定精度十分重要。星敏感器的低频误差是影响卫星姿态确定精度的重要因素之一,主要是由空间周期性的热环境变化引起的。为进一步提高卫星姿态确定精度,对星敏感器的低频误差产生机理即星敏感器主光轴做周期性扰动进行了分析,设计了星敏感器低频误差补偿方案,建立了考虑星敏感器低频误差在内的组合定姿模型,利用拓维卡尔曼滤波(AEKF)对低频误差进行补偿,并引入RTS平滑滤波进一步提高姿态确定精度。仿真实验表明,设计的星敏感器低频误差补偿方案能有效对其进行补偿,提高卫星姿态确定精度。 相似文献
6.
A. Ian McLeod K. W. Hipel 《Stochastic Environmental Research and Risk Assessment (SERRA)》1995,9(3):171-205
Current methods of estimation of the univariate spectral density are reviewed and some improvements are made. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements, rather than competes with, the popular ARMA model building approach. A new diagnostic check for ARMA model adequacy based on the nonparametric spectral density is introduced. Additionally, two new algorithms for fast computation of the autoregressive spectral density function are presented. For improving interpretation of results, a new style of plotting the spectral density function is suggested. Exploratory spectral analyses of a number of hydrological time series are performed and some interesting periodicities are suggested for further investigation. The application of spectral analysis to determine the possible existence of long memory in natural time series is discussed with respect to long riverflow, treering and mud varve series. Moreover, a comparison of the estimated spectral densities suggests the ARMA models fitted previously to these datasets adequately describe the low frequency component. Finally, the software and data used in this paper are available by anonymous ftp from fisher.stats.uwo.ca. 相似文献
7.
半参数回归与模型精化 总被引:31,自引:4,他引:31
就一般情况给出了半参数平差的算法,并结合一种特定的情况,讨论了正规化矩阵半正定时的计算方法,给出了相应的公式,最后构造了一个模拟的平差问题,对半参数法和最小二乘法的计算结果进行了比较,计算表明,半参数法能够发现并识别模型误差或观测值中的系统误差。 相似文献
8.
《The Photogrammetric Record》2002,17(99):505-511
18th International Symposium of Cipa by D. M. Barber, p.505
48th Photogrammetric Week by A. T. Smart, p.506
5th Conference on Optical 3D Measurement Techniques by G. Hunter, p.509 相似文献
48th Photogrammetric Week by A. T. Smart, p.506
5th Conference on Optical 3D Measurement Techniques by G. Hunter, p.509 相似文献
9.
《The Photogrammetric Record》2002,17(99):523-547
Books reviewed:
S. Openshaw and R. J. Abrahart, Geocomputation
M. Craglia and H. Onsrud, Geographic Information Research: Transatlantic Perspectives
P. J. Halls, Spatial Information and the Environment
J. Sievers and T. Schneider, Second International Symposium on Geographical Names: Geonames 2000
J. C. Leachtenauer and R. G. Driggers, Surveillance and Reconnaissance Imaging Systems: Modelling and Performance Prediction
P. A. Longley, M. F. Goodchild, D. J. Maguire and D. W. Rhind, Geographic Information Systems and Science
Y. Q. Chen and Y. C. Lee, Geographical Data Acquisition
K. Borre, Plane Networks and their Applications.
A. Hirschfield and K. Bowers, Mapping and Analysing Crime Data: Lessons from Research and Practice
O. Koelbl, Proceedings of the Oeepe Workshop on Automation in Digital Photogrammetric Production
RICS Books, Vertical Aerial Photography and Derived Digital Imagery: Client Specification Guidelines
D. E. Reeve and J. R. Petch, Gis Organisations and People: A Socio-Technical Approach 相似文献
S. Openshaw and R. J. Abrahart, Geocomputation
M. Craglia and H. Onsrud, Geographic Information Research: Transatlantic Perspectives
P. J. Halls, Spatial Information and the Environment
J. Sievers and T. Schneider, Second International Symposium on Geographical Names: Geonames 2000
J. C. Leachtenauer and R. G. Driggers, Surveillance and Reconnaissance Imaging Systems: Modelling and Performance Prediction
P. A. Longley, M. F. Goodchild, D. J. Maguire and D. W. Rhind, Geographic Information Systems and Science
Y. Q. Chen and Y. C. Lee, Geographical Data Acquisition
K. Borre, Plane Networks and their Applications.
A. Hirschfield and K. Bowers, Mapping and Analysing Crime Data: Lessons from Research and Practice
O. Koelbl, Proceedings of the Oeepe Workshop on Automation in Digital Photogrammetric Production
RICS Books, Vertical Aerial Photography and Derived Digital Imagery: Client Specification Guidelines
D. E. Reeve and J. R. Petch, Gis Organisations and People: A Socio-Technical Approach 相似文献
10.
Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I 总被引:1,自引:0,他引:1
Sang-Il Lee 《Journal of Geographical Systems》2001,3(4):369-385
This research is concerned with developing a bivariate spatial association measure or spatial correlation coefficient, which
is intended to capture spatial association among observations in terms of their point-to-point relationships across two spatial
patterns. The need for parameterization of the bivariate spatial dependence is precipitated by the realization that aspatial bivariate association measures, such as Pearson's correlation coefficient,
do not recognize spatial distributional aspects of data sets. This study devises an L statistic by integrating Pearson's r as an aspatial bivariate association measure and Moran's I as a univariate spatial association measure. The concept of a spatial smoothing scalar (SSS) plays a pivotal role in this task.
Received: 07 November 2000 / Accepted: 02 August 2001 相似文献