• That is a critical point. And we used the second derivative to see that this critical point is a local maximum.

    这是一个临界点,考察它的二阶导数,会发现这确实是一个极大值点。

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  • Between flare opening and spinning period, the local maximum moisture zone is formed.

    在大喇叭口期和吐丝期之间,形成一个局部最高水分区域。

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  • Finally, those points with local maximum feature strength were selected as vertices of polygonal.

    最后将具有局部极大特征强度的点作为多边形的顶点。

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  • A stationary point which is neither a local maximum nor a local minimum point is called a saddle point.

    一个既不是局部极大点又不是局部极小点的平稳点称为一个鞍点。

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  • An expand PV interpolation algorithm is proposed to avoid getting into the local maximum. it can reduce misregistration rate.

    文中提出了改进的部分体积插值方法,有效地避免陷入局部极值,减少误配率。

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  • The targets are separated off from searching for local maximum, determining threshold and combining with the correlation of image frames.

    搜索局部极大值,确定阈值并结合图像帧间的相关性分离出目标。

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  • The inversion methods based on local linearization are usually lost in local maximum values, and they seriously depend on the selection of initial model.

    常规的局部线性化反演方法往往易于陷入局部极大值之中,且严重依赖于初始模型的选取。

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  • This method is a stable and approximate description. We can reconstruct signal by using alternative projection algorithm from the wavelet transform with local maximum value.

    小波变换局部极大值描述是信号的一种稳定的、近似的描述,可以利用交替投影算法从小波变换局部极大值描述来重构信号。

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  • Based on the multi scale wavelet transform of the orientation curves we can utilize the information of local maximum positions to detect and locate the corners. Experimental res...

    对边缘围线的方向曲线进行多尺度小波变换,利用变换结果的局部最大值信息检测和定位出图像角点。

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  • A filtering method based on wavelet transform with local maximum value was discussed. The method could distinguish signal from the noise according to the different features of scale change.

    小波变换局部极大值滤波方法利用信号与噪声不同的尺度变化特性来区分信号与噪声,将噪声从信号中分离出来。

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  • A filtering method based on wavelet transform with local maximum value was discussed. The method could distinguish signal from the noise according to the different features of scale change.

    小波变换局部极大值滤波方法利用信号与噪声不同的尺度变化特性来区分信号与噪声,将噪声从信号中分离出来。

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