생몰정보
소속
직위
직업
활동분야
주기
서지
국회도서관 서비스 이용에 대한 안내를 해드립니다.
검색결과 (전체 1건)
원문 있는 자료 (1) 열기
원문 아이콘이 없는 경우 국회도서관 방문 시 책자로 이용 가능
목차보기더보기
Title Page
Contents
Preface 8
Abstract 9
Chapter 1. Introduction 10
1.1. Introduction 10
1.2. Contributions 12
Chapter 2. Related Literature Review 13
2.1. Introduction 13
2.2. Appearance-Based Approaches 13
2.2.1. Colors and Shapes 14
2.2.2. Hand Features 14
2.2.3. The Viola-Jones Algorithm 15
2.2.4. Scale Invariant Feature Transform (SIFT) Features 16
2.3. 3D Hand Model-Based Approaches 16
2.4. Statistic & Syntactic Approaches 17
Chapter 3. System Architecture 19
3.1. Selection of Hand Gestures 19
3.2. System Architecture 20
Chapter 4. Hand Detection, Tracking, and Classification 22
4.1. Cursor Motion 22
4.2. Haar-Like Features 23
4.3. AdaBoost Learning Algorithm 28
4.4. Data Collection 32
4.5. Training and Testing 34
4.6. A Parallel Cascades Structure 35
4.7. Real Time Performance 36
4.8. Technique for Accurate Recognition 39
4.8.1. Preprocessing 40
4.8.2. Implementation 40
Chapter 5. Conclusion 42
References 43
Table 2.1. Statistical and syntactic approaches. 17
Table 4.1.The performance of the trained cascades of classifiers 35
Figure 2.1. Hand tracking using the color cue. 14
Figure 2.2. The "Visual Panel" system. 15
Figure 2.3. The block diagram of 3D hand model-based approaches. 17
Figure 3.1. Various hand postures selected for system implementation 19
Figure 4.1. Generation of image contour from RGB image 22
Figure 4.2. Effect of Median filtering 23
Figure 4.3. The extended set of Haar-like features 25
Figure 4.4. The weight compensation for different Haar-like features 26
Figure 4.5. The concept of "Integral Image" 27
Figure 4.6. The boosting process of the AdaBoost learning algorithm 30
Figure 4.7. Detect positive sub-windows using the cascade classifiers 32
Figure 4.8. Positive Samples 33
Figure 4.9. A fraction of the negative samples used in the training process 34
Figure 4.10. The parallel cascades structure for hand posture classification 36
Figure 4.11. Snaps of the real time results 39
Figure 4.12. Results for finding center and circle 41
초록보기 더보기
Hand gestures can be used for natural and intuitive human-computer interaction. To achieve this goal, computers should be able to visually recognize hand gestures from video input.
This thesis proposes a new architecture to interact with computers accurately and easily. This thesis optimizes pre-existing techniques of hand gesture recognition for mouse operation. Mouse operation has two parts, movement of the cursor and other mouse events like right mouse button and left mouse button. Color is used as a robust feature to generate a contour of hand and centroid of the contour or the center of palm is extracted and used for movement of cursor. Hue, Saturation and Value (HSV) color space is used in this regard. For other mouse operations the system was trained on Haar-like features and the gesture were classified through Adaboost classifier to recognize the gestures.
The Haar-like features can effectively catch the appearance properties of the hand gestures. The AdaBoost learning algorithm can significantly speed up the performance and construct an accurate cascade of classifiers by combining a sequence of weak classifiers. To recognize different hand postures, a parallel cascades structure is implemented. This structure achieves real-time performance and high classification accuracy.
Each gesture was connected with each of the mouse operation. An additional technique was added to improve the gesture recognition rate. It counts the number of fingers in a gesture to accurately classify a gesture.
원문구축 및 2018년 이후 자료는 524호에서 직접 열람하십시요.
도서위치안내: / 서가번호:
우편복사 목록담기를 완료하였습니다.
* 표시는 필수사항 입니다.
* 주의: 국회도서관 이용자 모두에게 공유서재로 서비스 됩니다.
저장 되었습니다.
로그인을 하시려면 아이디와 비밀번호를 입력해주세요. 모바일 간편 열람증으로 입실한 경우 회원가입을 해야합니다.
공용 PC이므로 한번 더 로그인 해 주시기 바랍니다.
아이디 또는 비밀번호를 확인해주세요
