基于规范化谱主元分析的微操作深度信息提取

Extraction of Depth Information for Micromanipulation Using Normalized Spectral Principal Component Analysis

  • 摘要: 提出了一种在目标平移和尺度缩放时均保持不变的谱分析方法——规范化谱图像,消除了微操作工具在工作域X-Y平面内大范围移动对算法鲁棒性的影响.同时,利用主元分析方法降低图像数据维数并抑制噪声的影响,提高了算法的实时性和测量精度.实验结果验证了上述方法的有效性.

     

    Abstract: This paper proposes a novel spectral analysis method named normalized spectral image,which is invariant to translation and scaling.The influence of large-scale movement of micro-manipulation tools in the testing window(X-Y plane) on the algorithm robustness can be eliminated by using normalized spectral images.Meanwhile,principal component analysis(PCA) technique is used to reduce the dimensions of data and to suppress noise,and the real-time performance and accuracy of the algorithm is improved.Experimental results demonstrate the effectiveness of the proposed method.

     

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