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Description: parzen窗法对图象进行扫描处理的c++源程序,可供大型图象处理程序调用.-method of image scanning the c source available for large-scale image processing procedure call.
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Size: 907 |
Author: 马燕 |
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Description: 在图像处理中经常涉及分类，多次有用到非参数估计。本程序用Parzen窗来估计，用随机高斯函数来作计策信号.-in image processing involves classification, the many useful to the non - parametric estimation. The procedure used to estimate Parzen window, using random Gaussian function to signal for the ploy.
Platform: |
Size: 58218 |
Author: 江佳 |
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Description: parzen窗法对图象进行扫描处理的c++源程序,可供大型图象处理程序调用.-method of image scanning the c source available for large-scale image processing procedure call.
Platform: |
Size: 1024 |
Author: 马燕 |
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Description: 在图像处理中经常涉及分类，多次有用到非参数估计。本程序用Parzen窗来估计，用随机高斯函数来作计策信号.-in image processing involves classification, the many useful to the non- parametric estimation. The procedure used to estimate Parzen window, using random Gaussian function to signal for the ploy.
Platform: |
Size: 58368 |
Author: |
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Description: Parzen窗法，由样本求概密估计，例为一维正态分布和双峰均匀分布，内附随机数生成的原理说明。-Parzen window method, by the sample density estimate for estimated Example for one-dimensional normal distribution and bimodal uniform distribution, random number generator containing a description of the principle.
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Size: 134144 |
Author: 赵瑞峰 |
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Description: 帕曾窗方法的描述，数学公式推导，及其在图像处理领域中的应用。-description of parzen method, the mathematical formula and its application in the field of image processing.
Platform: |
Size: 458752 |
Author: 赵小川 |
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Description: Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages-Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages
Platform: |
Size: 2544640 |
Author: yangs |
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