從少量視角接近的樹葉影像進行樹葉之切割、重建與分類

Leaf Segmentation, Reconstruction and Classification from A Few Images with Very Close Viewpoints

郭奕廷(Yi-Ting Kuo)

指導教授 ： 陳永盛

元智大學/電機通訊學院/電機工程學系/碩士(2009年)

摘要

在這篇碩士論文裡，我們將呈現有樹葉切割、樹葉3D模型的重建和樹葉分類的一個完整系統。我們只基於少量且非常相近視角的影像來完成這個課題，我們的影像取得只需要用普通的數位相機就可以辦到了，而且也不用事先做到相機校正。因為我們只需要用到少量的影像就能做到樹葉切割和樹葉3D模型的重建，因此我們系統對於擷取樹葉影像是非常簡單且有彈性的。在樹葉分類那部份，我們用的特徵是centroid-contour distance (CCD)並且我們也運用了circular-shift comparing 來計算兩片樹葉的相似度，因此我們系統可以達到不受影像位移、旋轉和放大縮小的影響而完成樹葉分類。而後，我們會展示一些令人振奮的實驗結果，對於樹葉切割幾乎可以趨近完美的把外型切割出來，對於樹葉分類實驗結果也是可以令人信服的。

關鍵字

樹葉切割、樹葉重建、樹葉分類

並列摘要

In this thesis, we present a complete system to extract leaves, recover their 3D positions and finally classify them based on leaf shape. We use only a few images with slightly different viewpoints to achieve the task. The images are captured by a general hand-held digital camera and no camera pre-calibration is required. Because only a few images with close viewpoints are sufficient to segment the leaves and recover their 3D positions, our system is flexible and easy to use in image acquisition. For leaf classification, we use the normalized centroid-contour distance as our classification feature and employ a circular-shift comparing scheme to measure the similarity, thus our system has the advantages of being invariant to leaf translation, rotation and scaling. We have conducted several experiments and the results are encouraging. The leaves are nearly perfectly extracted and the classification results are also acceptable.

並列關鍵字

leaf segmentation, leaf reconstruction, leaf classification

參考文獻

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