形態影像處理在下列的應用是特別有用的，在醫學影像處理、圖形辨識、工廠自動生產線監測等等，這些都是硬體上的應用，但價值都是太昂貴。換句話說，軟體來實驗又太慢，而這篇論文的目的是經由平行處理加速欲計算的影像資料。形態運算可以表示成集合和函示相關的形式，我們學習這兩者觀念性的差異性，也因此提供它們之間的橋樑。
這形態運算函數相關的基本定義是類似數位旋積。我們可以完成這形型態運算經由兩種不同的觀點。在我們的論文當中，我們實現兩種方法，輸出分解以及輸入分解方法，來實現影像形態的擴散運算。以輸出觀點來實現，這輸出分解的方法是類似重疊-且-儲存方法。若以輸入觀點來實現，輸入分解是類似重疊-且-累加方法。兩種方法建立在平行架構的表現，將處理資料定義為最大的使用量，並採用MMX暫存器作倍數資料的處理，這樣的作法將會很有效益的。實際的數據顯示我們將比32位元作法，在速度上快了3倍以上，我們也將分析出一個理論公式來趨近於實際量測時間。

Morphological image processing is especially useful in the applications of medical image processing, pattern recognition, and industry auto-inspection. Special hardware for morphological image processing are very expensive. On the other hand, the speed of software are too slow. The purpose of this paper is to speed up the software computations of morphological image processing by parallel processing on Pentium machine.
The morphological operation is similar to digital convolution. We can realize our parallel morphological operation on the Pentium machine by two different methods. They are output-decomposition and input-decomposition methods, similar to the procedure of overlap-and-save and overlap-and-add respectively. The above methods implemented on Pentium machine are proved very efficient with 64-bits parallelism. Our experimental results demonstrated they are twice faster than the 32-bits parallelism method. In addition to the simulation and the real time experiments, a set of theoretical formulas are derived to analyze our methods and are checking the actual measured time quite well.

第一章 簡介 1

第二章 數學形態學簡介 5
2.1 簡介 5
2.2 擴張和侵蝕 6
2.3 開放和閉鎖運算 10

第三章 快速形態運算 14
3.1 形態學數理定義 14
3.2 形態運算的一般形式 16
3.3 由兩個不同的觀點來實現 18
3.4 形態運算的平行特性 19
3.5 形態處理的兩種實做方法 20
3.5.1 輸出分解方法（The output-decomposition method） 21
3.5.2 輸入分解方法（The input-decomposition method） 23

第四章 演算法的實現 26
4.1 位元映射的光柵（Raster-Format）影像資料 26
4.2 輸出分解方法的實現（output-decomposition method） 27
4.3 輸入分解方法的實現 33

第五章 MMX科技介紹 38
5.1 MMX科技簡介及應用 38
5.2 有別於傳統暫存器的MMX暫存器 39
5.3 新的資料型態 41
5.4 指令的使用 42

第六章 實驗結果 49
6.1 實驗說明 49
6.2 執行的速度 51
6.3 兩個方法的比較 54

第七章 總結 75

文獻參考 77

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