A new recognition algorithm of small moving target based on multi-feature fusion is presented.
提出一种新的基于多特征融合的弱小运动目标识别方法。
The test result shows the recognition rate by using multi-feature fusion is much higher than that by using single feature.
实验结果表明利用多特征融合方法的识别率明显高于只利用单一特征的识别率。
A multi-feature fusion approach is proposed to express which region one pixel belongs to, and it is suitable for the uncertainty.
本文提出了一种多特征融合技术，将某一像素所属的区域用隶属度表示出来，很好地适应了这种物体边界的不确定性。
The final results indicate that the Multi-feature fusion method is stable, reliable, and can efficiently improve the accuracy and the ability of fault tolerance.
结果表明，多特征信息融合的目标识别方法具有良好的稳定性，准确性和可靠性，能够有效地提高图象分类识别系统的精确度与容错性。
A new terrain matching neural network algorithm mode is constructed by means of multi-feature fusion, which includes different statistical and geometrical features.
提出了基于神经网络实现多特征融合的地形匹配算法，充分利用地形的各种不同的统计特征和几何特征，构造了一种地形匹配网络模型。
A new terrain matching neural network algorithm mode is constructed by means of multi-feature fusion, which includes different statistical and geometrical features.
提出了基于神经网络实现多特征融合的地形匹配算法，充分利用地形的各种不同的统计特征和几何特征，构造了一种地形匹配网络模型。
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