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研究生:王勻駿
研究生(外文):Yun-Jiun Wang
論文名稱:基於階層式時序記憶的多角度手勢辨識方法
論文名稱(外文):Multi-Angle Hand Posture Recognition Based on Hierarchical Temporal Memory
指導教授:黃雅軒黃雅軒引用關係
指導教授(外文):Yea-Shuan Huang
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊工程學系碩士班
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:2012
畢業學年度:100
語文別:中文
論文頁數:46
中文關鍵詞:階層式時序記憶角度手勢辨識
外文關鍵詞:Hierarchical Temporal MemoryMulti-angle hand posture recognition
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在圖形辨識(Pattern Recognition)的領域內,角度變化一直以來都是影響辨識效果的主要原因之一。因此,本論文使用了階層式時序記憶(Hierarchical Temporal Memory, HTM),根據其演算法的特性,乃是藉由時間性的概念,將時間上的連續變化影像特徵進行歸納,構成”不變性特徵”,以克服角度變化的影響,進而建立本論文的多角度手勢辨識方法。
由網路攝影機輸入影像之後,輸入影像將個別經由膚色偵測(Skin Detection)、背景分離(Background segmentation)與邊緣偵測(Edge Detection)的處理,產生的三種輸出結果,藉由投票的方式,正確的取得手勢區域;接著,判斷此手勢區域是否擁有手之前臂部分,若存在前臂,則進行前臂分離的步驟,如不存在,則跳過此一步驟;將影像大小正規化之後,便可交由HTM學習模型進行訓練與辨識。經由實驗的結果證明,使用同一組訓練與測試資料,本論文提出的多角度手勢方法達到92.5%的辨識率,而相較於Adaboost和SVM兩個演算法的86.8%和85.5%辨識率,的確可以達到更好的辨識效果。

In the field of pattern recognition, angle variation plays an important role in producing effective recognition results. To overcome the angle variation problems, this thesis adopts the Hierarchical Temporal Memory (HTM). Based on the inherent property of the HTM algorithm which applies temporal information to organize the continuous change in time of image features in constructing their respective “invariant features”, a multi-angle hand posture recognition method is hence proposed in this thesis.

We first obtain input images from a webcam. The input images will then be individually processed by skin detection, background segmentation, and edge detection. The processed results are next combined with a voting method to acquire the correct hand posture region. If a forearm exists, a forearm segmentation step will be executed; otherwise it will be skipped. After normalization of the output images through the forearm segmentation step, the images are forwarded to HTM for learning and training the classifier model. Our experimental results show that when using the same set of training and test data, the proposed multi-angle hand posture recognition method can achieve 92.5% recognition rate, thereby resulting in better performance than the Adaboost algorithm that only achieved 86.8% recognition rate and SVM algorithm that only achieved 85.5% recognition rate.

摘要 ……………………………………………………………………………………….. i
Abstract …………………………………………………………………………………… ii
誌謝 ……………………………………………………………………………………… iii
目錄………………………….…….…………………………………………………….... iv
圖目錄 ……………………………………………………………………….…………... vi
表目錄 ………………………………………………………………………………..… viii
第一章 緒論 ………………………………………………………………………. 1
1.1 研究背景與動機 …………………………………………………………. 1
1.2 相關研究 …………………………………………………………………. 2
1.3 論文架構 …………………………………………………………………. 5
第二章 系統架構 …………………………………………………………………. 6
第三章 手勢影像前處理 …………………………………………………………. 8
3.1 膚色偵測 …………………………………………………………………. 8
3.2 背景分離 ………………………………………………………………... 11
3.3 影像處理方法 …………………………………………………………... 16
3.4 手部區域定位與前臂分離方法 ………………………………………... 17
3.5 影像正規化 ……………………………………………………………... 19
第四章 手勢辨識方法 …………………………………………………………... 21
4.1 階層式時序記憶網路架構 ……………………………………………... 22
4.2 節點運算 ………………………………………………………………... 23
4.3 階層運算 ………………………………………………………………... 29
4.4 結合辨識演算法 ………………………………………………………... 31
第五章 實驗與分析 ……………………………………………………………... 33
5.1 系統實驗環境 …………………………………………………………... 33
5.2 手勢資料庫建立…………… …………………………………………... 33
5.3 實驗結果 ……………………………………………………………….. 34
5.4 分析與討論 …………………………………………………………….. 40
第六章 結論與未來研究方向 ………………………………………………….. 41
參考文獻 ……………………………………………………………………………….. 42
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