經驗模態分解(Empirical Mode Decomposition)在一維訊號處理的成功，許多學者將其推廣至二維並研究。本文提出利用二維經驗模態分解(Bidimensional Empirical Mode Decomposition)處理影像，取其高頻分量並計算其影像能量作為一種新的聚焦函數，由本文研究結果比較，可以看出此自動聚焦函數性能(Accuracy, Range, Number of false maximum and Width)皆優於其他自動聚焦函數。將影像經過前處理後再經由二維經驗模態分解，取其高頻分量經過拉普拉斯遮罩，再經過一連串後影像處理進行邊緣偵測，此方法有效的抑制雜訊且有良好的邊緣偵測能力。利用此邊緣偵測方與圓形霍式轉換實現PCSS法為主樣板比對法為輔之微粒子追蹤演算法，實現直徑30~50μm微粒子定位及追蹤，以便未來應用於生物醫學相關之微粒子操縱。

Empirical mode decomposition has been successfully employed in one-dimensional signal processing. Recently, many researchers have extended this approach to two-dimensional signal processing. In this research, a bidimensional empirical mode decomposition is employed to process an image by taking the value of high frequency component in the image energy as a new focusing function. By comparing the results in this research, we observe that the performance of the autofocus function (Accuracy, Range, Number of false maximum, and Width) is better than others. By preprocessing the image data, doing the bidimensional empirical mode decomposition, and taking the value of the high frequency component after dealing with the Laplace mask, and then through a serial image processing, an edge detection technique is implemented. The research shows that this method has better abilities in suppressing the signal noise and doing the edge detection. Particles tracking algorithm mainly based on PCSS and supplemented by pattern matching is realized by utilizing both edge detection and circular Hough transformation. The present system achieves positioning and tracking particles of diameter from 30 to 50 μm and can be utilized in biomedical engineering in the future.

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