摘要： 提出采用主分量分析(PCA)方法确定目标在最大方差意义下主轴的旋转角度,并结合稳定性好的低阶矩特征实现目标的平移、尺度和旋转的不变性变换;然后利用独立分量分析(ICA)良好的目标特征提取能力提取目标的特征值;最后在特征空间通过Fisher分类器实现目标识别。实验证实了算法的准确性。

关键词: 目标识别, 不变性, 主分量分析, 独立分量分析

Abstract: A novel approach is proposed in this paper. First, principal component analysis (PCA) is used to determine a target’s major axis whosedirection has the maximal variance. Combined with stable low-order moments, the translation, scaling and rotation invariance of a target is achieved.Because of independent component analysis (ICA)’s superior feature extraction capabilities, it is used to extract target features of targets. Finally,target recognition is conducted based on Fisher’s. The experimental results demonstrate the robustness and accuracy of the proposed algorithm.

中图分类号: 

• TP391.4