三维目标识别技术已经成为当前图象识别研究的重要方向。
Three - dimensional target recognition has become an important direction in image recognition.
针对三维目标识别问题，提出了一种基于快速骨架提取的方法。
Aiming at the IR 3d target recognition question, a method based on fast skeleton extraction is proposed.
数字全息三维目标识别技术作为一种新的识别技术正在成为研究的热点之一。
Three-dimensional (3D) pattern recognition technology with digital holography is a novel recognition technology, which has attracted extensive attentions.
提出一种先估计姿态后识别目标的新方法，克服了传统三维目标识别算法的多义性问题。
A new method was proposed to recognize target after attitude estimation, which overcame the ambiguity problem of traditional 3d target recognition algorithms.
实验表明，该方法具有较好的准确性和实时性，对于提高三维目标识别的效率具有理论意义。
The experiment showed that this method has higher precision and better real-time performance, which is theoretically significant for improving the efficiency of 3d target recognition.
三维目标识别技术是电视跟踪系统中的关键技术之一，也是目前该领域亟待解决的一个难题。
Technique of three-dimensional object recognition is one of the most important techniques in video tracking system and also a difficult problem in it.
目前通过试验证明，基于退化模型的目标识别方法是可行的，并且可望在三维目标识别研究领域占有显著的地位。
And it can be expected to occupy the remarkable position in the research field of three-dimensional target recognition.
在进行三维飞机序列图像的目标类型识别中，采用本文提出的自适应算法进行迭代运算，可以快速准确地进行目标识别。
During the three-dimensional images of aircraft sequence types identified objectives, the purpose of this algorithm using iterative computation can rapidly and accurately identify a target.
三维目标的识别可以转化为二维问题，所以本文先研究二维飞机目标的识别，以验证可行性，然后将其推广到三维飞机目标识别。
Therefore, the paper firstly pays attention to the research on aircraft target recognition of two-dimensional, after verifying its feasibility, it extend the algorithm to three-dimensional condition.
最佳观测方位问题是计算机主动视觉研究的重要内容，广泛应用于计算机目标识别、摄影测量、三维场景重建等领域。
Next best view problem has become one of main influence factors in the vision research fields such as target recognition, stereo matching, visual tracking and scene reconstruction, etc.
最佳观测方位问题是计算机主动视觉研究的重要内容，广泛应用于计算机目标识别、摄影测量、三维场景重建等领域。
Next best view problem has become one of main influence factors in the vision research fields such as target recognition, stereo matching, visual tracking and scene reconstruction, etc.
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