請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10556完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏嗣鈞(Hsu-Chun Yen) | |
| dc.contributor.author | Che-Wei Liu | en |
| dc.contributor.author | 劉哲維 | zh_TW |
| dc.date.accessioned | 2021-05-20T21:39:04Z | - |
| dc.date.available | 2010-08-20 | |
| dc.date.available | 2021-05-20T21:39:04Z | - |
| dc.date.copyright | 2010-08-20 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-13 | |
| dc.identifier.citation | [1] P. Mistry, P. Maes, and L. Chang, “WUW - wear Ur world: a wearable gestural interface,” Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, Boston, MA, USA: ACM, 2009, pp. 4111-4116.
[2] R. Goshorn, D. Goshorn, and M. Kölsch, “The Enhancement of Low-Level Classifications for Ambient Assisted Living,” Behavioral Monitoring and Interpretation Workshop at the German Conference on Aritificial Intelligence, KI, 2008. [3] Q. Chen, N. Georganas, and E. Petriu, “Hand gesture recognition using Haar-like features and a stochastic context-free grammar,” IEEE Transactions on Instrumentation and Measurement, vol. 57, 2008, pp. 1562–1571. [4] Y. Chen and K. Tseng, “Multiple-angle hand gesture recognition by fusing svm classifiers,” IEEE International Conference on Automation Science and Engineering, 2007. CASE 2007, 2007, pp. 527–530. [5] T. Liu, K. Wang, A. Tsai, and C. Wang, “Hand posture recognition using hidden conditional random fields,” The IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, 2009. [6] J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” Ninth IEEE international conference on computer vision, 2003. Proceedings, 2003, pp. 1470–1477. [7] H. Zhou, D. Lin, and T. Huang, “Static hand posture recognition based on okapi-chamfer matching,” Real-Time Vision for Human-Computer Interaction, 2005, pp. 85–101. [8] C. Wang and K. Wang, “Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction,” Recent Progress in Robotics: Viable Robotic Service to Human, 2009, pp. 317-329. [9] D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, 2161–2168. [10] B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” International Journal of Computer Vision, vol. 77, 2008, pp. 259–289. [11] B. Leibe, E. Seemann, and B. Schiele, “Pedestrian detection in crowded scenes,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2005. [12] P. Viola and M.J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, May. 2004, pp. 137-154. [13] M. Kolsch and M. Turk, “Robust hand detection,” Proc. of the Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG), 2004. [14] P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. [15] B. Froba and A. Ernst, “Face detection with the modified census transform,” Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp. 91–96. [16] A. Just, Y. Rodriguez, and S. Marcel, “Hand posture classification and recognition using the modified census transform,” Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 2006, pp. 351–356. [17] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, Nov. 2004, pp. 91-110. [18] L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 524–531. [19] E. Ong and R. Bowden, “A boosted classifier tree for hand shape detection,” Face and Gesture Recognition, 2004, pp. 889–894. [20] J. Willamowski, D. Arregui, G. Csurka, C.R. Dance, and L. Fan, “Categorizing Nine Visual Classes Using Local Appearance Descriptors,” ICPR Workshop on Learning for Adaptable Visual Systems, 2004. [21] C. Wallraven, B. Caputo, and A. Graf, “Recognition with local features: the kernel recipe,” Ninth IEEE International Conference on Computer Vision, 2003, pp. 257–264. [22] R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning,” 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, 2003. [23] M. Weber, M. Welling, and P. Perona, “Unsupervised learning of models for recognition,” Computer Vision-ECCV 2000, 2000, pp. 18–32. [24] J. van Gemert, C. Snoek, C. Veenman, A. Smeulders, and J. Geusebroek, “Comparing compact codebooks for visual categorization,” Computer Vision and Image Understanding, vol. 114, Apr. 2010, pp. 450-462. [25] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, 2006, pp. 3–42. [26] K. Grauman and T. Darrell, “The pyramid match kernel: Discriminative classification with sets of image features,” Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, 2005. [27] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006. [28] P. Tirilly, V. Claveau, and P. Gros, “Language modeling for bag-of-visual words image categorization,” Proceedings of the 2008 international conference on Content-based image and video retrieval, 2008, pp. 249–258. [29] A. Oikonomopoulos, I. Patras, and M. Pantic, “An implicit spatiotemporal shape model for human activity localization and recognition,” 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009. [30] B. Leibe and B. Schiele, “Scale-invariant object categorization using a scale-adaptive mean-shift search,” Pattern Recognition, 2004, pp. 145–153. [31] J. Triesch and C. von der Malsburg, “Classification of hand postures against complex backgrounds using elastic graph matching,” Image and Vision Computing, vol. 20, 2002, pp. 937–943. [32] J. Triesch and C.V. Malsburg, “Robust classification of hand postures against complex backgrounds,” International Conference On Automatic Face and Gesture Recognition, Killington, 1996. [33] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” Computer Vision–ECCV 2006, 2006, pp. 404–417. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10556 | - |
| dc.description.abstract | 手勢辨識在人機互動領域是相當熱門的研究主題，因為手勢是人類自然的溝通方式。先前的研究主要是專注在固定尺寸的影像上，評估是否符合某手勢的特徵，而常見方法是使用機器學習的AdaBoost演算法尋找手勢的重要特徵。近年來，局部特徵演算法逐漸受到重視，因為具備許多重要性質的強健性，例如亮度、尺度、方向等等。因此本論文改進了以局部特徵為基礎的隱含形狀模型方法，並使用此方法來解決靜態手勢辨識問題。我們發現精確度相較於先前文獻方法增進，並且我們的方法具有偵測手勢的方向，以及可辨識不同角度的手勢等特點。最後，本論文採用的演算法執行時間近乎即時，可用於一般的靜態手勢辨識應用，或是作為動態手勢的基礎。 | zh_TW |
| dc.description.abstract | Hand gesture recognition has become increasingly popular in Human-Computer Interaction (HCI) research as gestures provide a natural way of communication. Previous research has focused on searching a fixed size sub-window by evaluating a subspace of feature space that is found from machine learning algorithms such as AdaBoost. In recent years, however, local features have become increasingly popular as they offer robustness in illumination of the environment, scale, and rotational invariance of the hand itself. In this thesis, we describe a novel method of static hand posture recognition that is based on an Implicit Shape Model (ISM) of local features. We find improvement in recognition accuracy over former methods. In addition, our algorithm enhances the sliding-window paradigm by providing useful information such as hand orientation and rotational invariance. The execution time of the algorithm is also provided in order to assess its potential to be incorporated into a near real-time posture recognition application or a hand gesture system module. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T21:39:04Z (GMT). No. of bitstreams: 1 ntu-99-R97921072-1.pdf: 16497018 bytes, checksum: a3ebf0d4736f770c4cf20d4058e55af4 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Feature Algorithms 4 2.1.1 Harr-like Feature 4 2.1.2 Modified Census Transform (MCT) 5 2.1.3 Scale Invariant Feature Transform (SIFT) 6 2.2 Classification Methods 8 2.2.1 AdaBoost Approach 9 2.2.2 Support Vector Machine Approach 9 2.2.3 Explicit Shape Model Approach 10 2.3 Visual Word Representation 10 2.3.1 Visual Codebook Generation 11 2.3.2 Vector Projection 12 2.3.3 Visual Word with Spatial Information 12 Chapter 3 Hand Posture Recognition with an Implicit Shape Model 14 3.1 Overview of Our Approach 14 3.2 Learning the Implicit Shape Model 16 3.2.1 Visual Vocabulary Construction 16 3.2.2 Hand Center Estimation 18 3.2.3 Record the Occurrence Vectors 19 3.3 Posture Recognition 24 3.3.1 Hand Center Prediction 24 3.3.2 Voting Procedure 25 3.3.3 Preprocessing the Images 29 3.3.4 Summary of Our Recognition Algorithm 30 Chapter 4 Experimental Results 33 4.1 Hand Posture Recognition System 33 4.2 Evaluation 34 4.2.1 Hand Posture Database 34 4.2.2 Results of Triesch Hand Posture Database 36 4.2.3 Evaluation of T. C. Liu Posture Database 40 4.2.4 Running Time Analysis 42 Chapter 5 Conclusion and Future Work 45 REFERENCES 47 | |
| dc.language.iso | en | |
| dc.title | 以隱含形狀模型為基礎之靜態手勢辨識 | zh_TW |
| dc.title | Static Hand Posture Recognition Based on an Implicit Shape Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 雷欽隆(Chin-Laung Lei),郭斯彥(Sy-Yen Kuo),莊仁輝(Jen-Hui Chuang) | |
| dc.subject.keyword | 隱含形狀模型,靜態手勢辨識, | zh_TW |
| dc.subject.keyword | Implicit shape model,hand posture recognition, | en |
| dc.relation.page | 52 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2010-08-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-99-1.pdf | 16.11 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。