| 研究生: |
陳舒泓 Chen, Shu-Hung |
|---|---|
| 論文名稱: |
利用景深於二維影像重建暨外型量測之研究 The Study for Construction of Two-Dimensional Image and Measurement of Three-Dimensional Contours by Using Depth of Focus |
| 指導教授: |
陳元方
Chen, Terry Yuan-Fang |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年: | 2004 |
| 畢業學年度: | 92 |
| 語文別: | 中文 |
| 論文頁數: | 74 |
| 中文關鍵詞: | 對焦 、三維量測 |
| 外文關鍵詞: | Three-dimensional measure, Focus |
| 相關次數: | 點閱:99 下載:10 |
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本文利用對焦搜尋輪廓的原理,發展一套可以自動量測外型、同時重建全場對焦影像之系統,經由載物平台的連續移動,擷取連續影像,找出影像對焦程度最高時,其位置相對於參考平面的距離,將此觀念由點延伸到面,就可建立物件的三維輪廓,獲得對焦資訊後,亦同時建立全場對焦影像。
本系統對於三維量測非常方便,可以解決顯微鏡經常無法全場對焦的問題,運用其所具有的全場性、非接觸性和即時性的等優點,在量測外型上有高度準確性,如果使用此系統來執行外型的量測工作,可以經由一次量測而得到全場的試件外型,增加量測上的效率和分析外型之準確度。
本文提出了ESML對焦程度估算法。加入了45°及-45°方向判斷後的ESML運算法,與傳統SML運算法比較,對於影像對焦的敏感度增加,失焦與對焦影像辨別能力加強,對系統的準確性有顯著提升。
This paper use the principle of“Shape from Focus”to develop one system that can automatic measure the profile of sample and build up the focus image at the some time. By continuous moving the stage, pick and fetch the continuous image, then find out the images that the degree of focus is highest, get the distance between the position of focus images and the reference plane. Extend this idea from points to all the surface,we can set up the three-dimensional profile of the sample. After gatting focus information, we can set up the whole focus image.
Our system is very convenient for three-dimension measure, it can solve the problem that the microscope is often unable get the whole focus image. And our system has high accuracy in measuring profile by Appling the advantage of full-field、untouchable and real-time. If we used the system to measure the full-field profile of the sample, the efficiency of the work and the accuracy of analysing will be increase.
Our research proposed the revising type of focus measure to estimate the degree of focus, named〝Enhanced-Sum-Modified-Laplacian (ESML)〞. we increase the positive and negative 45°direction determining to the ESML rule, and it is more sensitivity to differentiate the focus images than the traditional SML rule. The accuracy of the system is increasingly.
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