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   整体影像匹配 的翻译结果: 查询用时:0.285秒
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整体影像匹配
相关语句
  global image matching
     Global Image Matching Based on Constraint Satisfaction Neural Network
     基于约束满足神经网络的整体影像匹配
短句来源
     GLOBAL IMAGE MATCHING BASED ONDYNAMIC PROGRAMING
     基于动恣规划的整体影像匹配
短句来源
  相似匹配句对
     GLOBAL IMAGE MATCHING BASED ONDYNAMIC PROGRAMING
     基于动恣规划的整体影像匹配
短句来源
     Global Image Matching Based on Constraint Satisfaction Neural Network
     基于约束满足神经网络的整体影像匹配
短句来源
     The Model of Image Matching Algorithm
     影像匹配算法模型
短句来源
     Image Matching Based on Gentic Algorithms
     基于遗传算法的影像匹配
短句来源
     A Global Approach for Video Matching
     一种整体的视频匹配方法(英文)
短句来源
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  global image matching
Also note that for both clustering methods, the overall results for the eigen-image matching are better than those for global image matching.
      
The success in global localization is from global image matching.
      


:Image matching is the foundation of digital photogrammetry. Considering thematching points on the multi-epipolar lines, a global image matching methodbased on dynamic programming is presented in this paper.A new stereo matchingmodel is constructed and corrected into a simple one dimensional dynamic pregra-mming one.A diagonal is used to define the stages and the parallax differencesin combination with the correlation coefficients are used to define the cost func-tion.Thus, the global optimal matching results...

:Image matching is the foundation of digital photogrammetry. Considering thematching points on the multi-epipolar lines, a global image matching methodbased on dynamic programming is presented in this paper.A new stereo matchingmodel is constructed and corrected into a simple one dimensional dynamic pregra-mming one.A diagonal is used to define the stages and the parallax differencesin combination with the correlation coefficients are used to define the cost func-tion.Thus, the global optimal matching results based on dynamic programmingare obtained.

影像匹配是数字摄影测量工作的基础。考虑到多条核线上的匹配点,本文提出了一种基于动态规划的整体影像匹配方法,构造了一种新的立体匹配模型,并将该模型转化成为简单的一维动态规划模型,采用对角线的方法划分阶段,用视差较和相关系数来构造阶段的代价函数,从而得到了基于动态规划的整体最优匹配结果。

The key technique to automatically extract the digital terrain model (DEM) from image pairs or stereo pairs is the image matching process. In this paper, the authors describes an approach to using constraint satisfaction neural network to solve the global image matching. The authors firstly give a simple description of the image matching and the constraint satisfaction problem. Then the authors outline an analogy method between image matching process and constraint satisfaction problems and a technique to construct...

The key technique to automatically extract the digital terrain model (DEM) from image pairs or stereo pairs is the image matching process. In this paper, the authors describes an approach to using constraint satisfaction neural network to solve the global image matching. The authors firstly give a simple description of the image matching and the constraint satisfaction problem. Then the authors outline an analogy method between image matching process and constraint satisfaction problems and a technique to construct the constraint satisfaction neural network in order to solve the global image matching. The authors utlimate goal is to get the accuracy and robust matching results, given a complicated terrains aerial photo stereo pairs. So the authors improve the traditional algorithm by use of the new relaxation algorithm presented by Levy [1998]. This algorithm can cope with the so called “zero matching” and “multi matching” problem, locate the regions of “zero matching”, and bridge the non texture areas. At last, the authors also give some experimental results to show the algorithms efficiency, accuracy and robust.

将影像匹配看作一个约束满足问题(CSPs),并用约束满足神经网络(CSNN)来实现整体影像匹配。根据新松弛标号法对网络的结构和迭代方式进行了改进,使其能够处理复杂地形条件下影像匹配中存在的“零匹配”和“多匹配”问题。实验表明,该匹配算法可快速、有效地处理复杂地形条件下的影像匹配问题

This paper proposes a global image-matching algorithm based on variable weight smooth constraint. The traditional image-matching algorithm can be divided into area-based, feature-based and relational image-matching algorithm. The area-based image-matching algorithm could obtain accurate and dense disparity surface, but this kind of algorithm depends on the texture information of the images, so it always obtains poor results in Poor-texture areas of images. However the feature-based image-matching algorithm...

This paper proposes a global image-matching algorithm based on variable weight smooth constraint. The traditional image-matching algorithm can be divided into area-based, feature-based and relational image-matching algorithm. The area-based image-matching algorithm could obtain accurate and dense disparity surface, but this kind of algorithm depends on the texture information of the images, so it always obtains poor results in Poor-texture areas of images. However the feature-based image-matching algorithm can obtain good results. So we should integrate both algorithms in the practical image-matching works. In the algorithm we integrate the area-based and feature-based image-matching algorithms by use of a well-defined objection function, by minimizing this function we get global image-matching algorithm based on variable weight smooth constraint. The algorithm evaluates smooth constraint and the discontinuance on linear feature. In poor texture area or texture area of the image, the weight of smooth constraint can vary based on the contents of image. In poor-textured area of the image, we impose stronger smooth constraint,and get the smooth disparity surface. Near the texture area, the strength of smooth constraint varies on the strength and orientation of the linear features. So it can correctly use the gray and feature information of image. Later we also give a direct mapping method between global image matching and Hopfield neural network. By comparing the energy function of Hopfield network with the so-called global compatible objection function,we get the bias input and the weight between neurons. So the image matching process can be completed efficiently, accurately and robustly because of the parallel processing of the Hopfield neural network. At the last portion of this paper,we provide some experimental results obtained by processing the practical image data including the images of rough terrain areas and the large-scale urban areas. The results show that the algorithm is effective.

提出了一种可充分利用影像灰度信息和特征信息的基于视差变权空间连续性约束的整体影像匹配方法,并将其映射到 Hopfield网络,利用神经网络所固有的大规模并行处理能力,可以高速、可靠、高精度地完成影像匹配,并大大提高了复杂地形条件下影像匹配的可靠性。实验证明该算法具有一定的实用性。

 
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