Wear debris found in gear lubricating oil provides extremely valuable information on the nature and severity of gear faults as well as remaining gear life. The conventional off-line process of taking samples of oil for testing of wear debris is a hindrance because it is laborious, expensive, delays information collection, and is expert oriented. In view of these limitations, the development of automating wear debris particle analysis using various approaches has been ongoing for years. However, existing online technology does not encourage widespread use of wear debris analysis (WDA) in the industry. High costs coupled with expert and labour requirements have led users to use other types of condition-based maintenance, such as vibration. There is a need to develop a WDA technique that is relatively cheap, online, requires little expertise to handle, and provides more information for maintenance decision-making. This PhD thesis proposes a WDA technique which uses image processing and three-dimensional image reconstruction to diagnose the health of machinery. Its emphasis is on using the thickness and volume of the particles generated over time to predict the onset of gearbox failure, so that maintenance action can be taken before gears reach catastrophic failure.

在三維模型的建構上，對應點的選取和改善佔有相當重要的地位。對應點的準確性影響整個建模的成效。本論文中我們提出了新的方法，透過極線轉換法(epipolar transfer)在多視角影像中做可見影像過濾和對應點改善。首先，我們以Furukawa所提出的方法，建構三維補綴面並加以做旋轉和位移，或是單純在二維影像移動對應點兩種方式選取初始對應點。然後再以本研究所提出的極線轉換法找到適當位置的對應點。接下來我們將每個三維點的可見影像(visible image)再次透過極線轉換法去檢查可見影像上的對應點位置是否適當，利用門檻值將不合適的對應點過濾掉。進一步針對對應點位置的改善和篩選，期望透過極線幾何法來找到位置最準確的對應點位置。最後比較實驗成果，觀察到以本研究所提出的方法做改善後，對應點準確度提高近百分之十五。 / In the construction of three-dimensional models, the selection and refinement of the correspondences plays a very important rule. The accuracy of the correspondences affects modeling results. In this paper, we proposed a new approach, that is filtering the visible images and improving the corresponding points in multi-view images by epipolar transfer method. First of all, we use Furukawa proposed method to construct three-dimensional patches and making rotation and displacement, or simply move the corresponding points in two-dimensional images are two ways to select the initial corresponding points. And then to use epipolar transfer method in this study to find the appropriate location of the corresponding points. Next we will check the corresponding points on the each 3D point’s visible image again through the polar transformation method , and we use the threshold value to filter out the corresponding points. Further the location of the corresponding points for the improvement and screening, hoped that through the epipolar geometry method to find the most accurate corresponding points’ location. Experimental results are compared to observe the improvements that the method proposed in this study, the corresponding point accuracy by nearly 15 percent.

近年來諸多學者專家致力於從多視角影像獲取精確的點雲資訊，並藉由點雲資訊進行三維模型重建等研究，然而透過多視角影像求取三維資訊的精確度仍然有待提升，其中萃取影像對應與重建三維資訊方法，是多視角影像重建三維資訊的關鍵核心，決定點雲資訊的形成方式與成效。 本論文中，我們提出了一套新的方法，由多視角影像之間的幾何關係出發，萃取多視角影像對應與重建三維點，可以有效地改善對應點與三維點的精確度。首先，在萃取多視角影像對應的部份，我們以相互支持轉換、動態高斯濾波法與綜合性相似度評估函數，改善補綴面為基礎的比對方法，提高相似度測量值的辨識力與可信度，可從多視角影像中獲得精確的對應點。其次，在重建三維點的部份，我們使用K均值分群演算法與線性內插法發掘潛在的三維點，讓求出的三維點更貼近三維空間真實物體表面，能在多視角影像中獲得更精確的三維點。 實驗結果顯示，採用本研究所提出的方法進行改善後，在對應點精確度的提升上有很好的成效，所獲得的點雲資訊存在數萬個精確的三維點，而且僅有少數的離群點。 / Recently, many researchers pay attentions in obtaining accurate point cloud data from multi-view images and use these data in 3D model reconstruction. However, this accuracy still needs to be improved. Among these researches, the methods in extracting the corresponding points as well as computing the 3D point information are the most critical ones. These methods practically affect the final results of the point cloud data and the 3D models so constructed. In this thesis, we propose new approaches, based on multi-view geometry, to improve the accuracy of corresponding points and 3D points. Mutual support transformation, dynamic Gaussian filtering, and similarity evaluation function were used to improve the patch-based matching methods in multi-view image correspondence. Using these mechanisms, the discrimination ability and reliability of the similarity function and, hence, the accuracy of the extracted corresponding points can be greatly improved. We also used K-mean algorithms and linear interpolations to find the better 3D point candidates. The 3D point so computed will be much closer to the surface of the actual 3D object. Thus, this mechanism will produce highly accurate 3D points. Experimental results show that our mechanism can improve the accuracy of corresponding points as well as the 3D point cloud data. We successfully generated accurate point cloud data that contains tens of thousands 3D points, and, moreover, only has a few outliers.