提出了一种基于机器视觉与离散傅里叶变换的目标特征识别方法。利用计算机图像技术采集和处理图像信号；
A identifying image methods based on machine vision and DFT are studied, The images are collected and video signal is processed using computer technology.
然后采用区域统计、参数识别、噪声区域去除以及聚类分析等手段进行目标特征识别，提取出棒材的质心点坐标作为特征；
Object centroid as the features was computed by means of regional statistics, parameter recognition, noise region removal and cluster analysis.
改进目标识别的新传感器以及其他目标识别和辨别特征。
New sensors for improved target identification; and other target recognition and identification features.
极化特征用于目标识别的一项主要研究是将极化信息与高分辨率雷达技术相结合。
The study on the application of polarization characteristics to the object identification is combined with the polarized information and the high-resolution Radar technology.
极化特征用于目标识别的一项主要研究是将极化信息与高分辨率雷达技术相结合。
A main study on the application of polarization characteristics to the object identification is to combine the polarized information with the high-resolution Radar technology.
特征提取直接关系到雷达的目标识别性能。
Feature extraction for radar high range resolution profiles (HRRP) recognition is concerned.
提出一种新的基于多特征融合的弱小运动目标识别方法。
A new recognition algorithm of small moving target based on multi-feature fusion is presented.
轴频是目标的特征之一，可以应用于目标识别当中。
Shaft frequency is one character of the target which can be applied to underwater targets identification.
目标的识别与分类的关键是提取有效的目标类别特征。
The key step of automatic classification is the extraction of target features.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm. Sometimes the features of target and noise are very alike.
前一种方法是利用信号在频域的能量分布特征识别目标，实际测试取得约83%的平均识别率。
The preceding method is uses the signal in the frequency range energy distribution trick recognition goal, the actual test obtains approximately 83% average recognition rate.
仿真结果表明：系统能提供更高精度的数据，获得目标的更多特征信息，并可对目标实施有效识别。
The simulation results indicate that the system can provide higher accuracy data, get more feature information of targets and perform effective target identification.
研究了宽带高分辨雷达目标识别中的特征压缩问题。
The problem of feature compression in high-resolution radar target recognition is studied.
该文详细讨论了基于HRRP的雷达自动目标识别的关键技术及研究现状，包括雷达hrrp的特性、预处理方法、特征提取方法及分类器设计方法等。
In this paper, we review the present research status and some key techniques in this field, including the property of HRRP, preprocessing methods, feature extraction and classifier design.
特征提取是目标分类和识别的基础。
Feature extraction builds up the basis of target classification and recognition.
螺旋桨轴频是被动声纳极其重要的目标识别特征之一。
The propeller shaft frequency is one of the important target recognition features for passive sonar.
目标噪声特征提取是被动声纳目标识别系统的关键技术。
Feature extraction of targets radiated-noise is the key technique of passive sonar target recognition system.
在分析研究空中目标特征指标的基础上，提出并建立了基于灰色关联的目标识别模型。
After analyzing the feature indexes of air targets, a target identification model based on grey relation is presented.
本文研究了雷达目标识别过程中关于目标的编码、分类特征的提取与选择等问题。
A new method for extracting radar target pole features from its RCS is presented in this paper.
空间弹道目标的光学强度序列分析与识别特征的提取。
The exo-atmospheric ballistic target's optical signature and recognition feature is studied.
形状是图像中目标的基本内在特性，是用于目标识别的重要特征，因此基于形状的目标识别方法研究具有重要意义。
Shape is the inherence characteristic of an object in the image, and it is the important character used for the object recognition.
提出了基于结构特征的空间目标识别算法。
A space target recognition algorithm based on structure feature is suggested in this paper.
利用最优子空间能够提取到更优的特征，改善目标识别性能。
The optimal subspace is used to extract feature of target for improving classification performance.
计算结果可用于目标特征提取、识别和分类，满足工程精度需求。
The result can meet engineering precision requirement and can be used for the target optical signature extraction, recognition and classification.
光传感器目标识别则是在取得目标的反射特性和辐射特性（光谱）后提取其特征进行识别。
Optical sensor target recognition extracts the characteristic of target to perform target recognition after having acquired its reflection and radiation (spectra) characteristics.
本文给出了一种基于角特征的目标兴趣区的识别算法。
A fast recognition algorithm is presented for interesting region extraction of an object based on corner features.
对于海面图像，分别采用感兴趣舰船目标区域的方差值、目标和背景亮度对比度这两个特征对目标进行融合识别。
As to sea image, the variance feature of region of interest and the luminance contrast feature between target and background are used to fusion recognition.
介绍了国内外特征提取和目标识别的一般情况，概述了几种目标识别和特征提取的方法，并对其进行筛选。
At the same time, it introduced the common complexion of character distilling and aim identification at home and abroad, and summarized some ways and filtrated for them.
目标噪声特征提取和目标分类器设计是被动声呐目标识别系统的关键技术。
Feature extraction of targets radiated noise and design of targets classifier are key technique of passive sonar target recognition system.
然后，以特征系数作为识别特征量，采用最小距离分类法，实现了自动目标识别。
Then, the feature coefficients are classified by the minimum distance criterion to recognize the target automatically.
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