一個基於花與葉片之植物辨識系統

本篇論文中我們提出一個基於花與葉片之植物辨識系統，針對台中都會公園的植物，利用照相手機所拍攝的花朵影像及葉片影像進行辨識。辨識系統共分為三大部分。第一部分為花朵辨識系統，總共使用了花朵的十四種特徵，包含三種外型以及十一種顏色的特徵。為了從複雜的背景中擷取出花朵的部分，我們提出了一個快速自動化切割物體的方法，並藉由使用者互動方式擷取出花朵部分。第二部份為葉片辨識系統，共使用了葉片的五個外型特徵。葉片自動辨識系統分為兩個階段，第一階段先將與輸入影像差異大的種類刪除，而第二階段依據第一階段過濾後的數種名單進行最後的辨識動作。系統的第三部份為針對開花植物中結合花朵以及葉片的辨識系統。首先，分別先將花朵以及葉片進行辨識。接著，我們提出了一個有效結合花朵以及葉片資訊的辨識方法，進行進一步的辨識，可有效提高辨識的準確率。

關鍵字

植物辨識；花朵辨識；葉子辨識

並列摘要

In this thesis, we propose a plant recognition system based on leaf and flower images. The images are taken in the Taichung Metropolitan Park by the camera mobile phone. There are three parts in the recognition system: flower, leaf, and combination of flower and leaf. In the flower recognition part, 14 features of flowers, including 3 shape features and 11 color features, are used. We propose a fast and automatic object segmentation method and combine user’s interaction to extract the flower region. In the leaf recognition part, 5 shape features are extracted. A two-stage approach is provided for automatic leaf recognition. Firstly, some impossible species are pruned according to the first three features. Next, the remaining species are tested to do recognition based on all five features. In the combining recognition part, a pair of leaf and flower images are recognized respectively. Then, an effective method is presented to do recognition by combining the recognition results of leaf and flower. According to experimental results, the combining recognition part can improve the recognition rate effectively.

並列關鍵字

Plant Recognition ； Flower Recognition ； Leaf Recognition

參考文獻

[11] C.L. Lee and S.Y. Chen, “Classification of Leaf Images,” International Journal of Imaging Systems and Technology, Vol. 16, No. 1, pp. 15-23, Jul. 5, 2006.

[2] T. Saitoh and T. Kaneko, “Automatic Recognition of Wild Flowers,” Systems and Computers in Japan, Vol. 34, pp. 90-101, 2003.

[3] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1: pp. 281-297, 1967.

[4] L. J. Chen, “A Fast Automatic Segmentation Algorithm based on Local Color-Distribution,” Master Thesis, Institute of Multimedia and Engineering, National Chiao Tung University, Taiwan, ROC, 2007.

[7] J. Zou and G. Nagy, “Evaluation of Model-Based Interactive Flower Recognition,” Proc. International Conference on Pattern Recognition, Vol. 2, pp. 311-314,

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一個基於花與葉片之植物辨識系統

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