隨著資訊科技的進步，人們越來越有機會帶著具有照相功能的行動科技產品例如智慧型手機，出外郊遊並且接觸到許多植物，而如何利用手中的科技產品辨別植物的種類，逐漸變成一個受到重視的課題。樹葉是植物中最容易取得的重要成分之一，因此常被用來當作辨識植物種類的主要標的物。本論文提出一個樹葉影像分類系統，以樹葉影像的特徵為分類的基礎，擷取其特徵後建立資料庫，以支援向量機作為分類器，利用主成分分析選取所降低的特徵向量的維度。由實驗結果顯示出本系統所提出的分類方法具有不錯的效果。

Plant recognition based on features of a leaf image is an attractive issue in the recent years. In this thesis, a leaf image classification system was proposed. For an input leaf image, the image is firstly preprocessed by using the digital image processing algorithm to extract the features as the input vectors of the Support Vector Machine. The Support Vector Machine is trained to classify leaf images. We also use Principal Component Analysis to reduce the dimension of the input vectors. Then we analyses to choose which dimension is proper to train the Support Vector Machine. From the experimental results, it is shown that the proposed method can perform well.

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