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研究生:洪建平
研究生(外文):Jian-ping Hung
論文名稱:鑰匙辨識之評估與比較
論文名稱(外文):Assessment and Comparison of Recognizing the Shape of Keys
指導教授:李建興李建興引用關係
指導教授(外文):Chien-hsing Lee
學位類別:碩士
校院名稱:國立成功大學
系所名稱:系統及船舶機電工程學系碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:76
中文關鍵詞:主動輪廓模型類神經網路最接近特徵線
外文關鍵詞:Active Contour ModelNearest Feature LineNeural Networks
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本文透過電荷耦合元件擷取鑰匙彩色影像,並將該彩色影像灰階化,再選用二維Haar小波轉換降低灰階化影像維度。然後,以主動輪廓模型來擷取鑰匙的輪廓,進而利用不變矩法擷取鑰匙影像的七個不變量特徵,以及搭配鑰匙輪廓所含之面積、週長與長短比等三種特徵,整合成鑰匙影像的十個特徵值。除此之外,亦利用簽名演算法尋找鑰匙灰階化影像的特徵值。最後,將所得之特徵值搭配最接近特徵線、類神經網路與歐氏距離等三種決策法則,用以評估與比較不同辨識方式於鑰匙辨識的辨識率。
The purpose of this thesis is to evaluate and compare different ways of recognizing the shape of keys. A charge-coupled device (CCD) is first used to record a color photo of keys. The photo is then transformed into a greyed image with a two-dimensional Haar wavelet transform to reduce the size of the image. Moreove, a method based on active contour models is used to obtain the shape of keys. Once the shape of keys has been obtained, an invariant moment method is used to capture the feature of keys with seven invariant moments as well as the area, periphery and ratio of long-axes and short-axes of the shape of keys are included. Additionally, a signature algorithm is applied to capture the features of greyed keys. Finally, classification methods including the nearest feature line, neural network and euclidean distance are used for recognizing the keys.
誌謝 iii
目錄 iv
頁次 iv
表目錄 vi
圖目錄 vii
符號說明 ix
第一章 序論 1
1.1 研究動機 1
1.2 論文架構 2
1.3 研究方法 2
第二章 影像偵測的前處理 5
2.1 前言 5
2.2 Sobel邊緣偵測法 5
2.3雷登轉換法 7
2.4 小波轉換 9
2.4.1 一維小波轉換 10
2.4.2 二維小波轉換 10
2.5 主動輪廓模型 15
2.5.1 內在能量 16
2.5.2 外在能量 18
2.6 面積、周長、長短軸比 20
2.7 不變矩 22
2.8 簽名 25
2.9 主成份分析法 26
2.9.1 主成份分析原理 26
2.9.2 主成份分析用於辨識的原理 29
第三章 影像決策法則 33
3.1 前言 33
3.2 最接近特徵線決策法 33
3.3 類神經網路 36
3.3.1 倒傳遞類神經網路 36
3.3.2 倒傳遞學習法 39
3.4 歐氏距離 42
第四章 模擬與實驗結果 43
4.1 前言 43
4.2 影像的擷取 43
4.3 平面鑰匙模擬結果 53
4.3 立體鑰匙模擬結果 55
第五章 鑰匙辨識系統的實作介紹 57
5.1 前言 57
5.2 鑰匙辨識實作介紹 57
5.3 實際介面 61
第六章 結論與未來展望 69
6.0 結論 69
6.1 未來展望 70
參考文獻 72
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