基於偏最小平方法之靜態手勢辨識

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姓名

蔡雨芳(Yu-Fang Tsai) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於偏最小平方法之靜態手勢辨識
(Hand Posture Recognition Using Partial Least Squares)

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摘要(中)

手勢辨識之應用可滿足人機互動的需求，故基於影像之手勢辨識為重要的研究議題。以影像為基礎之手勢辨識，易受到環境及人為影響，例如：手勢大小、光線變化及視角變化。此外，手勢辨識系統應降低訓練及測試時間，以因應遇到影像資料量大和特徵維度高之問題。
在本論文中提出以偏最小平方法(partial least squares, PLS)降低Histogram of Oriented Gradients (HOG)的維度，再結合支持向量機(Support Vector Machine, SVM)並採用RBF核函式，於訓練階段對降維後的HOG徵描述子進行分類器之訓練。於測試階段，對新進影像擷取HOG特徵描述子，並使用在訓練階段所得的降維係數，進行對測試資料的降維，再將降維後的資料輸入SVM分類器內，以OAA分類法則決定最終辨識結果，此既能減少系統處理時間，又能有效增進辨識率。

摘要(英)

The needs of applications of hand posture recognition meets the demand for human-computer interaction, thus the vision based hand posture recognition is an important research topic. However, vision based hand posture recognition is vulnerable to environmental changes or human impacts, i.e. size of hand posture, lighting and view variations. In addition , a hand posture recognition system must process efficiently in training and testing stage to handle the large amount of image samples and high dimensional descriptors.
In this paper, we propose to apply partial least squares method (partial least squares, PLS) on dimensions of Histogram of Oriented Gradients (HOG) descriptors, and to train support vector machines (Support Vector Machine, SVM) with RBF kernel function. At the training stage, we perform dimension reduction HOG descriptors and then to train SVM classifiers. In the testing phase, after obtaining of HOG feature descriptors on testing images, and then we use the weight matrix from PLS for dimension reduction. To obtain the final classification result, we use SVM classifiers, followed by OAA decision rule. With these helps , this system can not only reduce both the training and testing time, but also improve the recognition rate.

關鍵字(中)

★ 手勢辨識
★ 偏最小平方法
★ 梯度方向直方圖
★ 支持向量機
★ 降維
★ 降低訓練時間

關鍵字(英)

★ Hand Posture Recognition
★ Partial Least Squares
★ Histogram of Oriented Gradients
★ Support Vector Machine
★ Dimension Reduction
★ Training Time Reduction

論文目次

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前言 1
1.2研究動機 1
1.3 研究方法 3
1.4 論文架構 3
第二章以視覺為基礎的手勢辨識 5
2.1 手勢辨識系統架構 5
2.2 手勢辨識文獻回顧 6
2.2.1建立自由度(Degree of Freedom, DOF)手勢模型的手勢辨識演算法現況 6
2.2.2 基於影像特徵為主的手勢辨識演算法現況 9
2.2.3 採行降維技術的手勢辨識方案 22
2.3 分類器(Classifier) 23
2.3.1 支持向量機(Support Vector Machine,SVM) 23
2.3.1.1 訓練階段 25
2.3.1.2 測試階段 28
2.4 總結 29
第三章特徵擷取與降維 30
3.1 特徵擷取 (Feature Extraction) 30
3.1.1特徵擷取演算法回顧 30
3.1.2 Scale Invariant Feature Transform (SIFT) 30
3.1.3 Histogram of Oriented Gradients (HOG) 32
3.1.3.1 C-HOG 33
3.1.3.2 R-HOG 33
3.2 偏最小平方法(Partial Least Squares, PLS) 35
3.3 總結 39
第四章本論文所提出之以SVM為基礎採用PLS對HOG進行降維之手勢辨識演算法 41
4.1系統流程概述 41
4.2訓練階段 42
4.2.1 Hand Segmentation 42
4.2.2 使用雙立方內插法Bicubic Interpolation縮放ROI 43
4.2.3 HOG特徵擷取 45
4.2.4 PLS降維 46
4.2.5 SVM 分類器kernel參數選定 50
4.3 測試階段 52
4.4 總結 53
第五章實驗結果與討論 54
5.1實驗環境及測試影像資料庫 54
5.2辨識系統效能評估 56
5.2.1 辨識系統的準確率分析 56
5.2.1.1 HOG 區塊參數設定對準確率之影響 56
5.2.1.2 SVM與PLS降低的維度之比較 58
5.2.2 時間複雜度評估 69
5.2.2.1 PLS降維 69
5.3 訓練及測試時間分析 69
5.4 總結 73
第六章結論與未來展望 74
參考文獻 75

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2013-7-24

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