对于大量的钮扣，或节点，随着连接线段或边缘线数量的增加，系统突然从离散的断开的小群跳转到一个巨大的连接群。
For a large number of buttons, or nodes, as the number of connecting lines or edges increases, the system rather suddenly jumps from disconnected small clusters to a giant connected cluster.
在最优滤波器理论基础上，推导出离散域的最优平滑算子，抑制了图像的分割错误、噪声和伪边缘的影响。
The smooth operator is inferred based on the Optimal Discrete Filter's theory, which can reduce the influence of noise, false edges and image error.
本文提出了一种基于离散二进小波变换的多尺度边缘检测和图像融合的方法，实现了特征级图像融合。
Regarding this, a novel feature level approach to image fusion is proposed based on discrete dyadic wavelet transform for multi-scale image edge detection.
文章提出了基于离散平稳小波变换和最佳阈值分割算法的织物疵点边缘检测方法。
This paper proposed a method for fabric defects edge detection based on discrete stationary wavelet transform (DSWT) and optimal threshold segmentation algorithm (OTSA).
整个处理过程分为两步：第一，采用离散正交多项式曲面拟合技术探测边缘位置；第二，运用松弛标定网突出有意义的边缘结构和压缩噪声边缘。
The whole process can be divided into two steps: First, the method of surface fitting with discrete orthogonal polynomial is applied to detection of edge position.
结果通过离散余弦变换，图像由空间域变到了DCT域，在DCT域用指数低通滤波器去噪，高通滤波器提取地面特征边缘信息。
The test data is processed with two kinds of filter. Results At compressing field noise restrain and edge extraction are realized.
结果通过离散余弦变换，图像由空间域变到了DCT域，在DCT域用指数低通滤波器去噪，高通滤波器提取地面特征边缘信息。
The test data is processed with two kinds of filter. Results At compressing field noise restrain and edge extraction are realized.
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