It is still a difficult problem to extract features from face images efficiently.
如何有效地从人脸图片中提取人脸特征一直是一个难题。
A new data fusion method to extract features from the local environment for mobile robot has been developed and implemented.
本文基于栅格地图和滚动视窗的控制方法，提出了一种提取机器人局部障碍物群环境特征的数据融合新方法。
In order to improve the recognition effect and on the basis of analyzing the characteristic of MSTAR SAR image, a method of discrete wavelet analysis is proposed to extract features.
为了进一步提高MSTARSAR目标的识别效果，在分析了MSTAR SAR图像特点的基础上，提出了一种利用离散小波分解提取目标特征的方法。
On the basis of analyzing shape features of tomatoes, leaves and branches, we extract features of roundness, slightness, concavity degree and denseness depending on Fourier description.
研究了西红柿、叶子、枝干的形状特征，通过傅立叶描绘子，提取基于傅立叶系数导出的如圆形度、细长度、凹度、密集度等形状特征。
Firstly, Wavelet packet transform is introduced to extract features of vibration measured data and information fusion of data layer is conducted by assembling feature vectors of different sensors.
首先应用小波包变换对结构振动测试数据进行特征提取，通过不同传感器特征向量的合成完成数据层融合；
Firstly, using pattern aggregation theoretical models to extract features, merge the features which have the similar contributions to text classification, then a new mapping feature space is formed.
首先采用模式聚合理论进行特征抽取，将对文本分类具有相似贡献的特征合并，映射为新的特征空间。
Firstly, using pattern aggregation theoretical models to extract features, merge the features which have the similar contributions to text classification, then a new mapping feature space is formed.
首先采用模式聚合理论进行特征抽取，将对文本分类具有相似贡献的特征合并，映射为新的特征空间。
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