博碩士論文 101582603 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:4 、訪客IP:3.238.90.95
姓名 古安徒(Togootogtokh Enkhtogtokh)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 一個用於虛擬鍵盤之手勢識別框架
(A Gesture Recognition Framework for Virtual Keyboard Applications)
相關論文
★ 基於edX線上討論板社交關係之分組機制★ 利用Kinect建置3D視覺化之Facebook互動系統
★ 利用 Kinect建置智慧型教室之評量系統★ 基於行動裝置應用之智慧型都會區路徑規劃機制
★ 基於分析關鍵動量相關性之動態紋理轉換★ 基於保護影像中直線結構的細縫裁減系統
★ 建基於開放式網路社群學習環境之社群推薦機制★ 英語作為外語的互動式情境學習環境之系統設計
★ 基於膚色保存之情感色彩轉換機制★ 分數冪次型灰色生成預測模型誤差分析暨電腦工具箱之研發
★ 使用慣性傳感器構建即時人體骨架動作★ 基於多台攝影機即時三維建模
★ 基於互補度與社群網路分析於基因演算法之分組機制★ 即時手部追蹤之虛擬樂器演奏系統
★ 基於類神經網路之即時虛擬樂器演奏系統★ 即時手部追蹤系統以虛擬大提琴為例
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 了解多媒體內容在不同環境下的使用,特別是在日常生活中實現智能和簡單的使用是目前最有用的研究課題之一。我們提出了一種有效的機器學習方法來分析多媒體內容處理手勢事件的檢測和辨識。我們的機器學習方法具有基於經過深入研究的技術的訓練可靠的機制,例如Procrustes分析,局部和全局表徵的組合,線性形狀模型,並且應用於SMART TV虛擬鍵盤。在手勢事件檢測中,先定義點集的空間配置對於幾何是重要的。通常,增加這些點具有線性增量計算複雜度的算法的穩建性提高。我們考慮局部變形的組成的參數化,其解釋了實體之間的形狀和全局變換之間的差異,這說明了特定形狀作為手指和手的整體放置。在這項研究中,我們處理手勢事件檢測特別是指尖手勢檢測,以獲得智能和先進的技術使用。我們最新的視覺鍵盤可以是目前 SMART TV遠程控制的取代品。它更便宜,因為我們不需要實體裝置像傳統鍵盤、遙控器等,並且也不需要提供能源,例如電池等。
深層結構學習(深度學習)技術的興起近年來受到人工智能(AI)的關注。自動編碼技術(Autoencoder)是有希望的方法之一,尤其是深層結構化架構,通常用於縮小尺寸。例如,無人駕駛車就是最好的使用深度學習的例子。當然,我們的人類視覺在我們的感知系統上也有類似的過程。在這裡我們提出了一般技術。特別地,在我們提出的神經科學動機的方法中,考慮了非平坦(彎曲)和嘈雜的解決非線性機器學習方法。從神經科學到了今天,我們對人類的看法有著重要的成果,我們的眼睛是如何工作的,特別是在通量條件下才能實現現實世界。在神經科學中,發現當我們的眼睛將信號傳遞給大腦時,大腦具有作為尺寸縮小處理的特殊過程。未來,機器將在觸摸,視覺,聽覺,味覺和氣味方面取得進步。計算機視覺的最終目標是使計算機具有人類眼睛和大腦的能力 - 甚至以某種方式超越和協助人類。它引導我們系統地研究微分幾何,使差分歧管理論。詳細地說,為了實現這一點,首先我們的數學模型必須將整體流形學習視為彎曲(非平坦)歧管案例,而現實世界數據則是雜訊處理模型受到最多關注。我們提出了融合數學模型,解決了曲率數據,異常值檢測,成本優化,自動參數選擇和样本外擴展,將其應用於機器。
異常值檢測的實際應用範圍廣泛,如機械故障,系統行為,人為錯誤,人口自然偏差,大數據,高維數據作為深度點雲和非線性(多維)學習等。我們主要集中在非線性線性學習應用程序作為尺寸減少由於自然要求識別雜訊,以保留有意義的主要數據。沒有這種技術,尺寸減小方法是不可能獲得雜訊數據的正確結果。在黎曼空間的自然結構中,已經被認為是高維數據,這意味著在更實際真實的情況下工作,例如3D點雲數據具有非常重要的開始機制。
摘要(英)
Understanding multimedia content uses on different context specially to achieve smart and easy usages in daily life is one of the most useful researching topics currently. We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method has training robust mechanisms as based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. The spatial configuration of a predefined set of points is important for Geometry in gesture event detection. Generally, increasing these points have improved robustness of algorithm with a linear incremental computational complexity. We consider the parameterization of composition of local deformation that accounts for the differences between shape across identities and global transformation that accounts for the overall placement of particular shape as fingers and hands. In this research, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries.
Rise of deep structured learning (deep learning) technique is taking much attention in recent years for artificial intelligence (AI). Auto encoding technique (Autoencoder) is the one of the promising approach especially with deep structured architecture, typically for the purpose of dimensional reduction. As example driverless car is the best usage of it. Naturally, our human vision has the similar process on our perception system. Here we proposed the general technique for it. Specially, non-flat (curved) and noisy tackling non-linear machine learning method is taken into account in our proposed method with Neuroscience motivations. From Neuroscience, today, we have the important results about human perception that is how do our eyes work with brain especially under flux conditions to perciept the real world. In Neuroscience, it is discovered as when our eyes transfer the signal to brain, the brain has the special process that is the dimensional reduction processing. In the future, machine will make advances in touch, sight, hearing, taste, and smell. The ultimate goal of Computer Vision is for computers to have capability of human eyes and brains-or even to surpass and assist the human in certain ways. It leads us to systematically study the differential geometry such that differential manifold theory. In detail, to accomplish it, first our mathematical model must consider the general manifold learning as curved (non-flat) manifold case and real world data as noise handling model are taken the most attention. We have proposed the fusion mathematic model which solved the curvature data, outlier detection, cost optimization, automatic parameter choosing, and out-of-sample extension to apply it for machine.
Practical application of outlier detection is widely ranged as mechanical faults, system behavior, human error, natural deviations in populations, big data, and high dimensional data as depth point clouds and non-linear (manifold) learning, etc. We mainly focused on non-linear learning application as dimensional reduction due to natural requirement to recognize noise in order to preserve the meaningful main data. Without this technique, dimensional reduction approaches is impossible to gain correct result on noise data. In the natural structure of Riemannian space, it is already considered the high dimensional data, which means working in more practical case, such as 3D point cloud data that is very important to have outset mechanism.
關鍵字(中) ★ SMART TV虛擬鍵盤 關鍵字(英) ★ Gesture event detection
★ Gesture event recognition
★ SMART TV Keyboard
論文目次
摘要 i
Abstract iii
Acknowledgement v
Table of Contents vi
List of Figures viii
List of Tables x
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Background 1
1.3 Dissertation Organization 2
Chapter 2 Related Works 3
2.1 Intelligent Mechanisms 3
2.2 Machine Learning Approaches 4
2.3 Manifold Learning Previous Methods 8
Chapter 3 Proposed Method 13
3.1 Linear Approach 13
3.1.1 Preleminaries 13
3.1.2 Procrustes Analysis Alignment 14
3.1.3 Local Deformation and Global Transformation 20
3.1.4 PCA for Linear Shape Model 21
3.1.5 Training 25
3.1.6 Active Appearance Model 27
3.2 Non-Linear Approach 29
3.2.1 Preleminaries 29
3.2.2 Mathematical Preleminaries 31
3.2.3 Density on Manifold 33
3.2.4 Density Preserving Map on Manifold 34
3.2.5 The Curvature of Manifold 36
3.2.6 The Density Estimation on Manifold 38
3.2.7 Outlier Detection Mechanism 40
3.2.7.1 Density Estimation Kernel Function on Riemannian Manifold 40
3.2.7.2 Manifold Density Factor 42
Chapter 4 Results and Discussions 45
4.1 Experimental setup 45
4.2 Datasets 45
4.2.1 Benchmark Datasets 45
4.2.2 Real-time Users 50
4.3 Outlier Detection Experimental Results 54
4.3.1 Performance Evaluation 54
4.3.2 Noisy Datasets for Manifold learning 55
4.3.3 Benchmark Datasets 57
4.3.4 Point Cloud Datasets 58
4.3.5 3D Reconstruction Point Cloud Datasets 59
4.3.6 Human Body Noise 3D Point Cloud Dataset 60
4.3.7 Quantity Analysis 61
4.4 Manifold Learning Experimental Results 62
4.4.1 Synthetic Data Sets 64
4.4.2 Curvature Data Sets 65
Chapter 5 Conclusions and Future Works 67
REFERENCES 69
參考文獻

[1] Crowley JL, Berard F, and Coutaz J, “Finger tacking as an input device for augmented reality,” in Proc. Int. Workshop Automatic Face and Gesture Recognition, Zurich, Switzerland, pp. 195-200, June 1995.
[2] Quek FKH, Mysliwiec T, and Zhao M, “Finger mouse: a freehand pointing computer interface,” in Proc. Int. Workshop Automatic Face and Gesture Recognition, Zurich, Switzerland, pp. 372-377, June 1995.
[3] Wu Y, Shan Y, Zhangy Z, and Shafer S, “VISUAL PANEL: From an ordinary paper to a wireless and mobile input device,” Technical Report, MSR-TR-2000 Microsoft Research Corporation, http://www.research.microsoft.com, October 2000.
[4] Wang RY and Popovi J, “Real-time hand-tracking with a color glove,” ACM SIGGRAPH 2009 papers, pp. 1-8, 2009.
[5] Christian VH and François B, “Bare-hand human computer interaction,” in Proc. 2001 workshop Percetive user interfaces, Orlando, Florida, USA, pp. 1-8, Nov 2001.
[6] Oka K, Sato Y, and Koike H, “Real-time gesture event detection tracking and gesture recognition”, Computer Graphics and Applications, IEEE, vol. 22, pp. 64–71, Nov.-Dec. 2002.
[7] Sato Y, Kobayashi Y, Koike H, “Fast tracking of hands and gesture event detection in infrared images for augmented desk interface”, Proc. Fourth IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 462–467, 28-30 March 2000.
[8] Tomita A and Ishii JR, “Hand shape extraction from a sequence of digitized gray-scale images”, 20th Int. Conf. Industrial Electronics, Control and Instrumentation, 1994, IECON ′94, vol. 3, pp. 1925–1930, 5-9 Sept.1994.
[9] Keaton T, Dominguez SM, and Sayed AH, “SNAP&TELL: a multi-modal wearable computer interface for browsing the environment,” in Proc. Sixth Int. Symposium on Wearable Computers, 2002. (ISWC 2002), pp. 75–82, 7-10 Oct. 2002.
[10] Brown T and Thomas RC, “Finger tracking for the Digital Desk,” Proc. First Australasian User Interface Conference, 2000 pp. 11 – 16.
[11] Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artificial Intelligence Review 43:1-54
[12] Li F, Wechsler H (2005) Open set face recognition using transduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 27:1686-1697
[13] Baxter J (2000) A model of inductive bias learning. J Artif Intell Res 12:149–198
[14] Ren Y, Zhang F (2009) Hand gesture recognition based on meb-svm. In: Second international conference on embedded software and systems, IEEE Computer Society, Los Alamitos, pp 344–349
[15] Lee HK, Kim JH (1999) An HMM-based threshold model approach for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21:961-973
[16] Lu W-L, Little JJ (2006) Simultaneous tracking and action recognition using the pca-hog descriptor. In: The 3rd Canadian conference on computer and robot vision, 2006. Quebec, pp 6–13
[17] Francois R, Medioni G (1999) Adaptive color background modeling for real-time segmentation of video streams. In: International conference on imaging science, systems, and technology, Las Vegas, pp 227– 232
[18] Thirumuruganathan S (2010) A detailed introduction to K-nearest neighbor (KNN) algorithm. http://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm/
[19] Derpanis KG (2005) Mean shift clustering, Lecture Notes. http://www.cse.yorku.ca/~kosta/CompVis_Notes/mean_shift.pdf
[20] Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Kluwer, Boston 1–43
[21] Ramage D (2007) Hidden Markov models fundamentals, Lecture Notes. http://cs229.stanford.edu/section/cs229-hmm.pdf
[22] Charniak E (1993) Statistical language learning. MIT Press, Cambridge
[23] Senin P (2008) Dynamic time warping algorithm review, technical report. http://csdl.ics.hawaii.edu/techreports/08-04/08-04.pdf
[24] Andrea C (2001) Dynamic time warping for offline recognition of a small gesture vocabulary. In: Proceedings of the IEEE ICCV workshop on recognition, analysis, and tracking of faces and gestures in real-time systems, July–August, p 83
[25] Gavrila DM, Davis LS (1995) Towards 3-d model-based tracking and recognition of human movement: multi-view approach. In: IEEE international workshop on automatic face- and gesture recognition. IEEE Computer Society, Zurich, pp 272–277
[26] Sigal L, Sclaroff S, Athitsos V (2004) Skin color-based video segmentation under time-varying illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 862-877
[27] Wöhler C, Anlauf JK (1999) An adaptable time-delay neural-network algorithm for image sequence analysis. IEEE Transactions on Neural Networks, pp 1531-1536.
[28] Holzmann GJ (1925) Finite state machine: Ebook. http://www.spinroot.com/spin/Doc/Book91_PDF/F1.pdf
[29] J.B. Tenenbaum, V. D. Silva, J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science 290, vol. 5500, pp. 2319-2323, 2000.
[30] J.L. McFarland, A. F. Fuchs, "Discharge patterns in nucleus prepositus hypoglossi and adjacent medial vestibular nucleus during horizontal eye movement in behaving macaques," Journal of neurophysiology, vol. 68.1, pp. 319-332, 1992.
[31] J.S. Taube, "Head direction cells and the neurophysiological basis for a sense of direction," Progress in neurobiology, vol. 55.3, pp. 225-256, 1998.
[32] S.K. Ueno, T. Sunada, S. Morita, A mathematical gift: the interplay between topology, functions, geometry, and algebra, Tokyo: American Mathematical Soc, 2005.
[33] C. M. Bishop, M. Svensén and C. K. Williams, "GTM: The generative topographic mapping," Neural computation, vol. 10, no. 1, pp. 215-234, 1998.
[34] S. T. Roweis, L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, pp. 2323-2326, 2000.
[35] K.Q. Weinberger, L.K. Saul, “Unsupervised learning of image manifolds by semidefinite programming,” International Journal of Computer Vision, vol. 70, no. 1, pp. 77-90, 2006.
[36] R. R. Coifman, S. Lafon, "Diffusion maps," Applied and computational harmonic analysis, vol. 21, no. 1, pp. 5-30, 2006.
[37] M. Belkin, P. Niyogi., "Laplacian eigenmaps for dimensionality reduction and data representation," Neural computation, vol. 15, no. 6, pp. 1373-1396, 2003.
[38] D. L. Donoho, C. Grimes, "Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data," Proceedings of the National Academy of Sciences, vol. 100, no. 10, pp. 5591-5596, 2003.
[39] Z. Zhang, H. Zha, "Nonlinear dimension reduction via local tangent space alignment," Intelligent Data Engineering and Automated Learning. Springer Berlin Heidelberg, pp. 477-481, 2003.
[40] M. Brand, "Charting a manifold," Advances in neural information processing systems, pp. 961-968, 2002.
[41] F. Sha, L. K. Saul, "Analysis and extension of spectral methods for nonlinear dimensionality reduction," in ACM, 2005.
[42] L. Tong, H Zha. "Riemannian manifold learning," Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 796-809, 2008.
[43] B. Mikhail, P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural computation, vol. 15, no. 6, pp. 1373-1396, 2003.
[44] L. K. Saul, S. T. Roweis, "Think globally, fit locally: unsupervised learning of low dimensional manifolds," The Journal of Machine Learning Research, vol. 4, pp. 119-155, 2003.
[45] C.F. Gauss, General Invistigations of Curved Surfaces. New York, Raven Press, 1965.
[46] J.M. Lee, Riemannian Manifolds: an introduction to curvature. Invistigations of Curved Surfaces. Springer Press, 2006.
[47] Yan, Yan, Elisa Ricci, Ramanathan Subramanian, Oswald Lanz, and Nicu Sebe (2013). "No matter where you are: Flexible graph-guided multi-task learning for multi-view head pose classification under target motion." In Computer Vision (ICCV), 2013 IEEE International Conference on, pp. 1177-1184.
[48] Yan, Yan, Haoquan Shen, Gaowen Liu, Zhigang Ma, Chenqiang Gao, and Nicu Sebe (2014). "GLocal tells you more: Coupling GLocal structural for feature selection with sparsity for image and video classification." Computer Vision and Image Understanding, pp 99-109.
[49] Yan, Yan, Gaowen Liu, Elisa Ricci, and Nicu Sebe. (2013) "Multi-task linear discriminant analysis for multi-view action recognition." In Image Processing (ICIP), 2013 20th IEEE International Conference on, pp 2842-2846.
[50] Sebestian Marcell. Hand Posture and Gesture Dataset. http://www.idiap.ch/resource/gestures/
[51] Cambridge Hand Gesture Dataset. http://www.iis.ee.ic.ac.uk/icvl/ges_db.htm
[52] Ross A, Procrustes analysis, Technical Report, Department of Computer Science and Engineering, University of South Carolina, SC 29208
[53] Baggio, Daniel Lélis. Mastering OpenCV with practical computer vision projects. Packt Publishing Ltd, 2012.
[54] Gross R, Matthews I, and Baker S, “Generic vs.Person Specific Active Appearance Models.” Image and Vision Computing, vol.23 no. 11, pp. 1080-1093, 2005.
[55] Vidal R, Ma Y “Generalized Principal Component Analysis” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, pp. 1945-1960, 2005.
[56] Mosier CI. Determining a simple sturcture when loadings for certain tests are known.Psychometrika, vol.4, pp. 149–162, 1939.
[57] Schönemann PH. A generalized solution of the orthogonal Procrustes problem. Psychometrika, vol.31 no.1 pp. 1-10, 1966.
[58] Green B. The orthogonal approximation of an oblique structure in factor analysis. Psychometrika vol.17 no.4, pp. 429-440, 1952.
[59] Hurley JR, Cattell RB, Producing direct rotation to test a hypothesized factor structure, Behavioral Science vol.7, no.2, pp. 258–262, 1962.
[60] Cliff N. Orthogonal rotation to congruence. Psychometrika, vol.31, no.1, pp. 33-42, 1966.
[61] Schönemann PH, Robert MC. Fitting one matrix to another under choice of a central dilation and a rigid motion. Psychometrika, vol.35, no.2, pp. 245-255, 1970.
[62] Kristof W, Wingersky B. A generalization of the orthogonal Procrustes rotation procedure to more than two matrices. Proceedings of the Annual Convention of the American Psychological Association. American Psychological Association, 1971.
[63] Gower JC. Generalized procrustes analysis. Psychometrika vol.40 no.1, pp. 33-51, 1975.
[64] Berge T, Jos MF. Orthogonal Procrustes rotation for two or more matrices. Psychometrika, vol.42, no.2, pp. 267-276, 1977.
[65] Lingoes JC, Ingwer B. A direct approach to individual differences scaling using increasingly complex transformations. Psychometrika, vol. 43, no.4, pp. 491-519, 1978.
[66] Green BF, Gower JC. A problem with congruence. Annual Meeting of the Psychometric Society, Monterey, California. 1979.
[67] Berge T, Jos MF, Dirk LK. Orthogonal rotations to maximal agreement for two or more matrices of different column orders. Psychometrika, vol.49, no.1, pp. 49-55, 1984.
[68] Peay ER. Multidimensional rotation and scaling of configurations to optimal agreement. Psychometrika, vol.53, no.2, pp. 199-208, 1988.
[69] Commandeur JJ. Matching configurations. DSWO Press, Leiden University, pp. 13-61, 1991.
[70] Dijksterhuis GB, Gower JC. The interpretation of generalized procrustes analysis and allied methods. Food Quality and Preference, vol.3, no.2, pp. 67-87, 1992.
[71] Kiers HAL, ten Berge JMF. Minimization of a class of matrix trace functions by means of refined majorization. Psychometrika, vol.57, no.3, pp. 371-382, 1992.
[72] Gower J. Orthogonal and projection procrustes analysis. 1995.
[73] Everson R. Orthogonal, but not orthonormal, procrustes problems. Advances in computational Mathematics, 1998.
[74] Gower, JC, Dijksterhuis GB. (2004) Procrustes problems. Oxford: Oxford University Press.
[75] Gruen AW, Akca MD. (2003) Generalized procrustes analysis and its applications in photogrammetry.
[76] Igual L, Perez-Sala X, Escalera S, Angulo C, Dela TF. Continuous generalized procrustes analysis. Pattern Recognition, vol.47, no.2, pp. 659-671. 2014.
[77] Cauchy AL. Sur l’équationa l’aide de laquelle on détermine les inégalités séculaires des mouvements des planetes. Exer. de math, vol.4, no. 1, pp. 74-195.
[78] Stewart GW. On the early history of the singular value decomposition. SIAM review vol.35, no.4, pp. 551-566, 1993.
[79] Beltrami E. On bilinear functions. SVD and Signal Processing, pp. 9-18. 1873
[80] Jordan C. Mémoire sur les formes bilinéaires. Journal de mathématiques pures et appliquées, pp. 35-54. 1874
[81] Preisendorfer, Rudolph W. Principal component analysis in meteorology and oceanography. Ed. Curtis D. Mobley. Vol. 425. Amsterdam: Elsevier, 1988.
[82] Fisher RA, Winifred AM. CP32 Studies in crop variation, II: The manurialresponse of different potato varieties. Journal of Agricultural Science, Cambridge13, pp. 311-320, 1923.
[83] Pearson K. Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol.6, no.2, 1901.
[84] Hotelling H. Analysis of a complex of statistical variables into principal components, Journal of educational psychology, vol.24, no.6, 1933.
[85] Anderson, TW, Gupta SD. Some inequalities on characteristic roots of matrices. Biometrika, pp. 522-524, 1963.
[86] Rao CR. The use and interpretation of principal component analysis in applied research. Sankhyā: The Indian Journal of Statistics, Series A, pp. 329-358, 1964.
[87] Gower JC. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, vol.53, pp. 325-338, 1966.
[88] Jeffers JNR. Two case studies in the application of principal component analysis. Applied Statistics, pp. 225-236, 1967
[89] Atchle WR, Edwin HB. Multivariate statistical methods, among-groups covariation. Dowden, Hutchinson & Ross,Halsted Press, 1975.
[90] Preisendorfer, Rudolph W. Principal component analysis in meteorology and oceanography. Ed. Curtis D. Mobley. Vol. 425. Amsterdam: Elsevier, 1988.
[91] Karhunen J, Jyrki J. Representation and separation of signals using nonlinear PCA type learning. Neural networks, vol.7, no.1, pp. 113-127, 1994.
[92] Mika S, Schölkopf, B, Smola AJ, Müller KR, Scholz M, Rätsch, G. Kernel PCA and De-Noising in Feature Spaces. In NIPS, vol. 4, no. 5, 1998.
[93] Cardoso JF. High-order contrasts for independent component analysis. Neural computation, vol.11, no.1, pp. 157-192, 1999.
[94] Chennubhotla C, Allan J. Sparse PCA. Extracting multi-scale structure from data. Computer Vision, ICCV 2001. Proceedings. Eighth IEEE International Conference on. vol.1, 2001.
[95] Hubert M, Sanne E. Robust PCA and classification in biosciences. Bioinformatics, vol.20, no.11, pp. 1728-1736, 2004.
[96] Forbes K, Eugene F. An efficient search algorithm for motion data using weighted PCA. Proceedings of the 2005 ACM SIGGRAPH. ACM, 2005.
[97] Lu H, Plataniotis KN, Venetsanopoulos AN. MPCA: Multilinear principal component analysis of tensor objects. Neural Networks, IEEE Transactions on, vol.19, no.1, pp. 18-39, 2006.
[98] Jolliffe L. Principal component analysis. John Wiley & Sons, Ltd, 2002.
[99] Edwards, Gareth J, Christopher JT, Timothy FC. Interpreting face images using active appearance models. Automatic Face and Gesture Recognition, Proceedings. Third IEEE International Conference on. IEEE, 1998.
[100] Cootes TF, Gareth JE, Christopher JT. A Comparative Evaluation of Active Appearance Model Algorithms. BMVC. vol. 98, 1998.
[101] Baker S, Matthews I. Equivalence and efficiency of image alignment algorithms. In Computer Vision and Pattern Recognition, CVPR 2001, vol.1, pp. I-1090. IEEE, 2001.
[102] Hou XW, Li SZ, Zhang H, Cheng Q. Direct appearance models. In Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol.1, pp. I-828, IEEE, 2001.
[103] Abdulameer MH, Sheikh ASNH, Othman ZA. A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony. The Scientific World Journal, 2014.
[104] Wu Y, Ma B, Yang M, Zhang J, Jia, Y. Metric learning based structural appearance model for robust visual tracking. Circuits and Systems for Video Technology, IEEE Transactions on, vol. 24 no.5, pp. 865-877, 2014.
[105] Cootes TF, Kittipanya-ngam P. Comparing Variations on the Active Appearance Model Algorithm. In BMVC, pp. 1-10, 2002.
[106] Song G, Ai H, Xu GY. Hierarchical direct appearance model for elastic labeled graph localization. In Third International Symposium on Multispectral Image Processing and Pattern Recognition, pp. 139-144, 2003.
[107] Quach KG, Duong CN, Luu K, Le HB. Gabor Wavelet-Based Appearance Models. In Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp. 1-6, 2012.
[108] Papandreou G, Maragos P. Adaptive and constrained algorithms for inverse compositional active appearance model fitting. In Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1-8, 2008.
[109] S.H. Sebastian, D.D. Lee, “Manifold ways of perception,” Science 290, vol. 5500, pp. 2268-2269, Dec. 2000.
[110] J.B. Tenenbaum, V. D. Silva, J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science 290, vol. 5500, pp. 2319-2323, 2000.
[111] J.L. McFarland, A. F. Fuchs, "Discharge patterns in nucleus prepositus hypoglossi and adjacent medial vestibular nucleus during horizontal eye movement in behaving macaques," Journal of neurophysiology, vol. 68.1, pp. 319-332, 1992.
[112] J.S. Taube, "Head direction cells and the neurophysiological basis for a sense of direction," Progress in neurobiology, vol. 55.3, pp. 225-256, 1998.
[113] S.K. Ueno, T. Sunada, S. Morita, A mathematical gift: the interplay between topology, functions, geometry, and algebra, Tokyo: American Mathematical Soc, 2005.
[114] J. Moser, "On the volume elements on a manifold," Transactions of the American Mathematical Society, pp. 286-294, 1965.
[115] C.F. Gauss, General Investigations of Curved Surfaces. New York, Raven Press, 1965.
[116] J.M. Lee, Riemannian Manifolds: an introduction to curvature. Investigations of Curved Surfaces. Springer Press, 2006.
[117] AT&T, “The Database of Faces,” http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. 2002.
[118] “The Georgia Tech Face Database,” http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. 2000.
[119] “ISOMAP Face data,” http://isomap.stanford.edu/face_data.mat.Z, 2008.
[120] P.Bruno, "Kernel Density Estimation on Riemannian Manifold," Statiistics and Probabilty Letters, vol. 73, pp. 297-304, 2005.
[121] J.Nigmegen, "Nonparametric estimation of a probability density on a riemannian manifold using fourier expansions," The Annals of Statistics, vol. 18, pp. 832-849, 1990.
[122] O. Arkadas, A.G. Gray, "Submanifold density estimation," In Advances in Neural Information Processing Systems, pp. 1375-1382, 2009.
[123] E. Parzen, “On estimation of a probability density function and mode,” The Annals of Mathematical Statistics, pp. 1065–1076, 1962.
[124] T. Cacoullos, “Estimation of a multivariate density,” Annals of the Institute of Statistical Mathematics, vol.18, pp.179–189, 1966.
[125] L. Stan. Encyclopedia of Biometrics: I-Z. Vol. 1. Springer Science & Business Media, 2009.
[126] M. Balasubramanian, E.L. Schwartz, J.B. Tenenbaum, V. de Silva, and J.C. Langford, “The Isomap Algorithm and Topological Stability,” Science, vol. 295, pp. 7a, Jan. 2002.
[127] F. Provost and T. Fawcett, “Robust Classification for Imprecise Environments”, Machine Learning, vol. 42, pp. 203-231, 2001.
[128] L.J Latecki, A. Lazarevic, and D. Pokrajac. “Outlier detection with kernel density functions”, In International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 61-75, July. 2007.
[129] N. Roussopoulos, S. Kelly and F. Vincent, ”Nearest Neighbor Quries,” Proc. ACM SIGMOD, pp. 71-79, 1995
[130] M.Yunqian, and Y. Fu. “Manifold learning theory and applications”, CRC press, 2011.
[131] T. Enkhtogtokh, and T.K Shih, “Multimedia content analysis on gesture event detection for a SMART TV Keyboard application”, Multimedia Tools and Applications, pp.1-23, 2016
[132] M. Breuning, H-P. Kriegel, R. Ng, and J. Sander. LOF: Identifying Density-Based Local Outliers. In Proc. of 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD’00), Dallas, Texas, pp 93- 104, 2000
[133] N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R∗-tree: an efficient and robust access method for points and rectangles. In Proc. of 1990 ACM SIGMOD International Conference on Management of Data (SIGMOD’90), pp 322-331, Atlantic City, NJ,1990.
[134] Latecki, Longin Jan, Aleksandar Lazarevic, and Dragoljub Pokrajac. "Outlier detection with kernel density functions." International Workshop on Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2007.
[135] Sebestian Marcell. Hand Posture and Gesture Dataset. http://www.idiap.ch/resource/gestures/
[136] Cambridge Hand Gesture Dataset. http://www.iis.ee.ic.ac.uk/icvl/ges_db.htm
[137] F. Provost and T. Fawcett, Robust Classification for Imprecise Environments, Machine Learning, 42, 3, pp. 203-231, 2001.
指導教授 施國琛(Timothy K Shih) 審核日期 2017-7-14
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明