局部最大值 meaning in Chinese
local maximum
Examples
- Mostly , traditional arithmetic of image edge detection distill edge using gradient maximum or zero - crossing which base on gray discontinuousness on edge . they are affected easily by noise
传统的边缘检测算子大都是基于边缘的灰度不连续性利用梯度局部最大值或二阶导数过零点来检测边缘，容易受噪声干扰。 - The writer analyzed mallat three criteria and introduced the optimized function based on these three criteria , at last found optimized mother wavelet for edge detection . at the end of this chapter single edge searching method based on local maximum is introduced and gained ideal edge image
分析mallat提出的评价边缘检测函数优劣的三个判别函数，利用这三个判别函数列出最优化方程，推导出边缘检测最优小波，最后创造性提出局部最大值单像素边缘搜索法，提取出单像素宽度边缘图像，图像效果达到了满意的效果。 - It is a markov network , we confirm the transfer rule of this network based on two kind of relationship , one is the image and the scene , the other is the current scene and its neighbour . learning parameters of this network come from training examples , we can obtain a exact local maximum of the posterior probability for the scene , thereby we generate a effective edge detection result for original blurry importing image
它的体系结构是一个马尔可夫网络，根据图像与景物、景物与景物之间的联系来确定网络上的信息传递规则，并从大量的事例中学习这些网络参数，可以高效地为所求景物找到一个精确的后验概率的局部最大值，从而为原模糊图像获得高效的边缘标记结果。 - Besed on wavelet " s well localized characterizes in time frequency and space frequency , we can exactly depict events of nonnoise by decomposing to signal using binary - wavelet transform . for events of noise , local max can be found at singular points of signal by using wavelet transform norm of signal , so adopting multiscale wavelet analysis the maximal point of wavelet transform norm , which risen by the noise , with increasing of scale , which rien by the fault appeared . gived a properly threshold and compared the relation of data that have been transformed not only find the fault in the low noise signal but also resume the signal by filtering noise , which have good power of examining singular signal being unexpectedly change
对于噪声中的事件点，利用信号的小波变换的模在信号的突变点取得局部最大值，采用多尺度(多分辨)小波分析，则随着尺度的增大，噪声引起的小波变换模的极大值点迅速减少，而故障引起的小波变换模的极大值点得以显露，我们给出一个适当的阀值，比较阀值和变换后数据的关系，不但可以在低信噪比的信号中监测到故障信号，而且可以滤去噪声恢复原信号，对突变的奇异性信号具有良好的监测能力，能够有效地找出噪声中的事件点。