Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7899
標題: 一基於輪廓模糊類神經網路之移動物體辨識系統
Moving Object Recognition By Contour-Based Neural Fuzzy Network
作者: 陳亮佐
Chen, Liang-Tso
關鍵字: Contour;輪廓;Neural Fuzzy Network;Moving Object;Recognition;模糊類神經網路;移動物體;辨識
出版社: 電機工程學系
摘要: 
本論文提出一個基於輪廓模糊類神經網路之移動物體辨識系統。在擷取移動物體方面,我們用了一連串的影像處理方法;這些方法包括:現有影像與背景影像之灰階值相減,Sobel運算,型態學處理。在獲取物體的特徵值方面,我們運用輪廓為基礎的模型。使用了輪廓追蹤以及離散傅立葉轉換(DFT)來獲取輪廓的特徵值。另外一個特徵值是長寬比,這個數值可以從擷取物體的垂直投影和水平投影獲得。最後我們使用自我架構類神經模糊推論網路(SONFIN)來訓練以及辨識移動物體。實驗結果顯示我們可以精確的分辨出行人、機車、汽車及狗四種移動物體,同時使用SONFIN的結果比使用類神經網路的辨識結果來得好。

Moving object recognition by contour-based neural fuzzy network is proposed in this thesis. To extract a moving object, we use a series of image processes, including gray-based subtraction between current and background images, Sobel operation, and morphological operation. To extract object's feature vector, we use contour-based model. Parts of the features are obtained by contour following followed by Discrete Fourier Transform (DFT). Another feature is length-width ratio, which can be derived from vertical and horizontal projection of the extracted object. Finally, we use the Self-Constructing Neural Fuzzy Inference Network (SONFIN) to train and recognize moving objects. The experiment shows we can recognize four moving objects, including a pedestrian, a motorcycle, a car, and a dog, exactly. The performance of SONFIN is shown to be better than a neural network from comparison.
URI: http://hdl.handle.net/11455/7899
Appears in Collections:電機工程學系所

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