三維數位化量測儀是層析式的逆向工程設備，經由逐層磨削後取得待測物的二維影像資料，再將影像重新堆疊成三維CAD模型。使用的影像需先利用門檻值切割法分離出目標影像，再經過影像型態學的處理後，才可用於模型重建。此模型重建流程重建單物件是可行的，但是使用此流程重建多物件時會產生影像誤差。
使用多門檻值切割多物件影像產生的影像誤差，包含有材質轉換區的影像誤差、影像輪廓邊界處的影像誤差與刀痕的影像誤差。材質轉換區的影像誤差來自於待測物或是鑲埋樹脂的透光性質，因透光的材質會使影像灰階值漸漸的轉換成另一種材質所屬的灰階值，且透光率越高時，受到材質轉換區的影響就越大。為解決材質轉換區的影像誤差，本研究根據最小平方法以計算切削方向的像素灰階值的分布趨勢線之斜率以辨別材質轉換的區域。而為加快斜率之計算，本研究提出一演算法可使程式的運作速度加快約1倍。經修正後的影像，約可去除總層數的4% ~ 10%(約18層 ~ 31層)之材質轉換區的影像誤差。修正材質轉換區後的影像再經過影像型態學的處理，可去除影像輪廓與切削刀痕產生的影像誤差，以供後續的應用。

A 3D digitizer can be used to perform reverse engineering. It takes photos of a working piece layer by layer. After the images are segmented, we can use these images to reconstruct a CAD model by computer software.
Although threshold method was useful to segment single object images, multi-threshold method did not work on multiple-object images. After multiple-object images were segmented by multi-threshold method, these images had 3 kinds of image errors. The first one was material transition, the second one was image profile’s boundary and the third one was tooling marks. Both material transition and image profile’s boundary were caused by the pixel’s gray level which was too similar to our target. Tooling marks were caused randomly by cutting tool.
In this study, we used linear regression method to correct image error. In our cases, the material transition was between 4% and 10 %. Then we used image morphology to correct images profile’s boundary and tooling marks. By the image processes from this study, we can now correct images from 3D digitizers, and thus we can offer a accurate images for our applications.

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