How to select the optimal threshold is a delicate matter.
如何选择最优阈值，是一个非常棘手的问题。
The selection of the optimal threshold of signal detection.
信号检测的最佳门限选择；
It divides an image into several parts and each part USES an optimal threshold scheme.
将原图像划分为若干子块，对每个子块利用最佳阈值的方法进行分割。
In image segmentation algorithms, the selection of optimal threshold is the key to segmentation.
在阈值分割算法中，确定最优阈值是图像分割的关键。
In preprocessing, optimal threshold value method and smooth technology are adopted to process sample form image.
预处理阶段，使用最佳阈值二值化方法和平滑技术处理样本表格图像。
That is, there exists an optimal threshold, which lies in the average gray of the joint transform power spectrum by computer simulation.
并通过计算机模拟，确定出该最佳阈值位于联合变换功率谱的平均灰度值附近。
Simulation results show that using CGA to find optimal threshold can save more operation time than GA and improve convergence probability.
实验结果表明，与遗传算法相比，混沌遗传算法用于阈值寻优减少了运算时间，提高了收敛率。
Finally, the output of this algorithm is the optimal threshold. Using this threshold to partition off the pixels, image segmentation is implemented.
最后，通过寻优搜索得到算法的输出值即为最优阈值，以此阈值划分像素，实现图像分割。
To solve the above problems, this paper adopts intelligent optimization algorithms to search for the optimal threshold, aiming to maximize the efficiency and accuracy.
为了解决以上问题，本文采用智能优化算法来搜寻最优阈值，旨在最大限度地提高寻优效率和寻优精度。
This paper proposed a method for fabric defects edge detection based on discrete stationary wavelet transform (DSWT) and optimal threshold segmentation algorithm (OTSA).
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。
For suboptimal method based on optimal threshold analysis, a recursive form of threshold selection using Newton iteration is developed with false alarm probability constraint.
次优门限算法以最优门限分析为基础，在虚警概率约束下使用牛顿迭代给出求解门限的递推算法。
Because the images edge can be affected by noise more easily than the region feature, defect region-detection is proposed by combining mathematics morphology and optimal threshold method.
鉴于图像边缘特征易受噪声影响，而区域特征受噪声影响要小得多，提出利用数学形态学和最佳阈值分割相结合的方法来实现缺陷区域提取。
Because the images edge can be affected by noise more easily than the region feature, defect region - detection is proposed by combining mathematics morphology and optimal threshold method.
鉴于图像边缘特征易受噪声影响，而区域特征受噪声影响要小得多，提出利用数学形态学和最佳阈值分割相结合的方法来实现缺陷区域提取。
Because the images edge can be affected by noise more easily than the region feature, defect region - detection is proposed by combining mathematics morphology and optimal threshold method.
鉴于图像边缘特征易受噪声影响，而区域特征受噪声影响要小得多，提出利用数学形态学和最佳阈值分割相结合的方法来实现缺陷区域提取。
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