邻域

The proof is given that the category of linear fuzzy neighborhood spaces in the sense of Katsaras is isomorphic to that of co-towers of topological vector spaces and the intersection of the category of linear fuzzy neighborhood spaces with the category of type fuzzy topological vector spaces is the extract category of induced fuzzy topological vector spaces.

证明了Katsaras的线性fuzzy邻域空间范畴与分明线性拓扑空间的余塔空间范畴同构，而Katsaras的线性fuzzy邻域空间范畴与型fuzzy拓扑线性空间范畴的交集恰为诱导的fuzzy拓扑线性空间范畴。

Analysis on UPCA model spatial sensitivityAgainst the situation that most urban CA model with no consideration of the spatial scale of data input, taking Changsha as the example, it analyzed the UPCA model spatial sensitivity, drew that the appropriate resolution and the neighborhood structure of Changsha area are respectively 30m and C-1 (taking a cell as radius circular neighborhood).

3UPCA模型的空间尺度敏感性分析针对目前大多城市CA模型在模拟城市空间形态演变时没有考虑输入数据的空间尺度影响，本研究论证UPCA模型模拟长沙市城市空间形态的空间尺度敏感性，得出了应用于长沙地区的适宜分辨率和邻域结构分别为30m和C-1(以一个元胞为半径的圆形邻域)，并基于此空间尺度获取了模型的最佳校验参数。

The increase of distance in circle neighborhood will decrease the model precision, in the mean while the increase of distance in rectangle neighborhood will increase the precision first then decrease.

环状邻域距离的增大会导致模拟精度的降低，面状邻域距离的增大则导致模拟精度先增后降。不同土地类型对邻域距离变化的敏感程度和反应皆不相同。

System structure of the Neighborhood Image Frame Buffer is presented,aiming at the problem of the data stream of high speed image processing,especially the storing and accessing of the neighborhood image data.

本文针对高速图像处理的数据流问题，特别是邻域图像数据的并行存取问题，提出了邻域图像帧存储体的体系结构，实现了帧存储体邻域图像数据的并行存取，极大地提高了图像处理的速度。

Then proposes a video image pre-processing method based on adaptive neighborhood statistics. The proposed pre-processing conquers the low efficiency of classical adaptive neighborhood filter and the sensitivity to threshold in other methods.

提出了一种基于自适应邻域统计的视频图像预处理算法，该算法解决了自适应邻域滤波中邻域构建的效率问题和传统预处理算法的门限敏感问题。

To solve this problem,we present a method of self-intersecting manifold learning. The core of this method is to select neighborhood points based on tangent space clustering. We construct a layered neighbor-graph to storage the trusted information of low dimensional neighborhood.

为了解决这个问题，本文提出一种自相交流形学习算法，其核心内容是基于切空间分类的邻域选取方法，主要是通过逐步建立分层邻域图储存正确的邻域关系，以获取有效的局部低维结构。

The method includes the following steps: firstly, a spherical neighborhood of present sample points is established by using the geometric spherical-modelling theory and all the sample points contained in the spherical neighborhood are adopted as candidate neighbor points, thus not only preserving the effectivity of the dimension reduction capability when data sets are sparse but also getting the advantages of low-sensitivity to isolated points and good stability of the preserved topological structure; then a data relevance matrix more matching semantics can be obtained by relevance measurement based on route clusters to update the candidate neighbor points in the spherical neighborhood and optimize the regular neighborhood space of the present sample points, thus improving the phenomenon that the dimension reduction of sample sets provided with folded curved faces is apt to suffer the integrated-structure distortion in case of heterogeneous data distribution.

首先利用几何开球原理建立当前样本点的球状邻域，将包含在球状邻域内的所有样本点作为候选近邻点，不但能够保持在数据集稀疏情况下的降维性能的有效性，而且具有对孤立点敏感性不高、保留拓扑结构稳定性好的优点。然后利用基于路径聚类的相关性度量得到更符合语义的数据相关性矩阵，用来对球状邻域内的候选近邻点进行更新，优化当前样本点的规则邻域空间，改善了当数据不均匀分布时在带有折叠弯曲面的样本集上降维容易出现整体结构扭曲的现象。

It is time-consuming. The dissertation basis the laser scan data organization form of two dimension grids, the three-dimensional point directly relation to vicinage region point, the new corresponding point will produce from the neighbor region.

本文根据激光扫描数据二维网格的组织形式，三维点的邻域关系直接体现在距离图像象素点的邻域关系上，因此对应点周围的点也有可能产生新的对应点，所以采用在已知对应点的邻域内局部搜索新的最近的点对，提高搜索的效率。

A parametric surface was reconstructed at a local area of each sample point, and a believing neighbor area was generated based on a given error. A new parametric surface was recomputed, and a larger believing area was generated. The surfel with largest believing area was reconstructed, and the original model was approximated by the surfel in an as large as possible local area.

该算法在每个采样点附近重建一个函数曲面，根据给定误差得到置信邻域，重新计算函数曲面，得到更大的置信邻域，如此反复迭代，产生一个具有最大置信邻域，并在更大范围内逼近原模型的面元。

First it selects a 3×3 neighborhood.According to whether gray value of arbitrary pixel is its neighborhood's extremum,all pixels are divided into doubtful noises pixels and signal pixels.Then it constructs 8 templets of orientation gradient for each doubtful noise,makes the sure of the noises sets according to the magnitude of 8 values of orientation gradient.

该算法首先对含有噪声的图像取3×3邻域，判断某点是否为邻域极值将全部像素点分为可疑噪声与信号点集合；其次对每一个可疑噪声点在其3×3邻域内构造八个方向的梯度算子模板，通过比较8个方向梯度的大小，进一步确定其是否为噪声点；最后对噪声点进行邻域的中值滤波。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。