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研究生:劉昆暢
研究生(外文):Kun-Chang Liu
論文名稱:結合膚色和骨架化之手掌辨識
論文名稱(外文):Hand Recognition by Combining Skin Colors with Skeletons
指導教授:陳文淵博士
指導教授(外文):Dr.Wen-Yuan Chen
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:82
中文關鍵詞:手掌辨識膚色骨架化角度偵測
外文關鍵詞:Palm RecognitionSkin ColorSkeletonCenter-of-gravityAngle Detection
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  隨著電腦科技進步,人機互動介面的改進一直是許多研究的課題,從早期的鍵盤和滑鼠,到現在的攝影機、手寫筆和手寫板等,都是為了讓使用者能夠更方便操作電腦。隨著使用體感控制器的Wii,在家用遊戲機市場上大熱賣,讓使用者以更直覺地方式遊玩遊戲,讓遊戲不再只是感覺在控制按鈕的動作而已。因此本文提出一種使用結合膚色和骨架化之手掌辨識法,使用辨識手掌姿勢來作為遊戲或機器的輸入裝置。
  本系統分為兩個部份:第一個部份為膚色偵測和影像分割,先將輸入影像和背景影像由RGB (Red,Green,Blue)色彩空間轉換成YCbCr色彩空間,依此色彩空間作膚色判定,取出手臂部份。第二部分為骨架化和影像辨識,將手臂之二值化影像做骨架化處理取得骨架線條,再使用侵蝕(Erosion)取出掌心和手臂點,以掌心為中心點畫圓和骨架線相交取出交叉點,再以掌心和手臂點為0度,計算出各交叉點的分佈位置度數,依此度數判別出是何手勢。
  經實驗結果證明本演算法,無論在標準的手掌姿態,或紐曲的手掌姿態下都可以正確辨識手勢結果,尤其是可辨識出非剪刀的錯誤二指之手勢。

In finger detection, we adopt four strategies to achieve the task: 1) skeletonization of the palm image as a binary image to avoid human intentionally gesture; 2) calculating the center-of-gravity of the palm image for use as the center of a circle for further computation of the number and angle of the fingers. The number of the fingers is counted by the intersection number of the palm skeleton and circle; 3) producing a base-line as the zero-axis for angle calculation. 4) comparing the degree of the two finger angle and threshold to decide whether the two fingers are a correct Scissors.
Simulation results demonstrate that our scheme always achieves correct identification, no matter whether it is a left or right hand, various combinations of extended fingers, different angles of the hand and/or fingers, different environments, or with noise interference.
中文摘要 i
Abstract iii
致 謝 iv
目 錄 v
圖 目 錄 vii
表 目 錄 x
第1章、 緒論 - 1 -
1.1研究背景 - 1 -
1.2研究動機與目的 - 3 -
1.3文獻探討 - 8 -
1.4章節概要 - 12 -
第2章、 數位影像處理 - 13 -
2.1 色彩空間轉換(Color Transfer) - 13 -
2.2 二值化(Binarlize) - 16 -
2.3 影像濾波器(Filter) - 18 -
2.4 形態學(Morphology) - 20 -
2.5 拓撲學(Topology) - 23 -
第3章、 手掌辨識演算法 - 24 -
3.1 手掌辨識流程 - 24 -
3.2 手掌分割演算法 - 25 -
3.2.1色彩空間轉換 - 25 -
3.2.2 膚色偵測 - 27 -
3.2.3 手掌擷取 - 28 -
3.3 手掌辨識演算法 - 34 -
3.3.1 骨架化 - 34 -
3.3.2 手掌重心 - 35 -
3.3.3 角度分析 - 37 -
第4章、 實驗結果 - 41 -
4.1實驗環境 - 41 -
4.2實驗結果 - 42 -
第5章、 結論與未來方向 - 66 -
5.1結論 - 66 -
5.2未來方向 - 67 -
參考文獻 - 68 -
作者簡介 - 71 -

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