Accurate segmentation of targets on complex background is one of the key techniques in Automatic target Recognition (ATR).
复杂背景下目标的准确分割是自动目标识别中的关键技术之一。
Target detection is the first stage of automatic target recognition (ATR).
目标检测是自动目标识别(atr)的第一个阶段。
Due to the characteristics of the underwater image, the ATR (automatic target recognition) included image collection, image processing, feature extraction, pattern recognition.
根据水下图像的特点，目标识别系统包括：图像采集，图像处理，图像特征提取，根据特征进行目标识别。
We contribute to automatic target recognition (ATR) by a hidden Markov model (HMM) based classifier, with parameters of scattering centers.
提出用散射点参数作为识别特征，用隐马尔可夫模型（HMM）作分类器的雷达目标识别方法。
The most commonly used evaluation measure in the domain of Automatic Target Recognition(ATR) algorithm evaluation is Accuracy(ACC), however, the ACC measure has many defects.
ATR算法评估领域常用的评估指标为正确识别率ACC，但ACC自身存在诸多缺陷，仅用ACC指标评估结论具有一定的盲目性和误导性。
The classical pattern arithmetic ATR (Automatic Target Recognition) is applied in the tracking system.
在软件算法上，本文采用经典的AT R技术对目标进行检测识别。
Azimuth estimation is very important in the field of Automatic Target Recognition(ATR) of Synthetic Aperture Radar(SAR) images.
方位角估计是合成孔径雷达（SAR）图像自动目标识别研究中的一个重要问题。
Azimuth estimation is very important in the field of Automatic Target Recognition(ATR) of Synthetic Aperture Radar(SAR) images.
方位角估计是合成孔径雷达（SAR）图像自动目标识别研究中的一个重要问题。
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