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研究生:張晃維
研究生(外文):Huang-Wei Chang
論文名稱:基於時空崁入的動態材質模擬
論文名稱(外文):Modeling Dynamic Textures with Spatio-Temporal Embeddings
指導教授:鄭國揚鄭國揚引用關係
指導教授(外文):Kuo-Young Cheng
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
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:2005
畢業學年度:93
語文別:英文
論文頁數:69
中文關鍵詞:高斯過程動態影像動態系統
外文關鍵詞:Dynamic TexturesGaussian ProcessesSpatio-temporalEmbeddingDynamical System
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We propose a spatio-temporal embedding framework for better modeling image sequences, known as dynamic textures, that exhibit certain stationarity in the appearance and the dynamics. Our method can characterize more types of data than those by the existing techniques established on linear dynamical systems (LDS). The algorithm finds a low-dimensional representation for the original data without discarding the critical time-dependent information. It then directly deals with the nonlinear dynamics through an integration of Gaussian process with time delay embedding. In addition, the possible nonlinearity in the appearance space is also appropriately addressed by our use of multiple local analyzers. Consequently, the framework is flexible enough for handling various applications, including prediction and smoothing. Results on prediction and smoothing are provided to demonstrate the advantages of our approach.
1 Introduction 1
1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Physics-Based Approaches . . . . . . . . . . . . . . . . . . . . 3
1.1.2 Image-Based Approaches . . . . . . . . . . . . . . . . . . . . . 3
1.2 Our Approach and Contributions . . . . . . . . . . . . . . . . . . . . 6
2 Modeling Dynamic Textures via Dynamical Systems 9
2.1 Introduction to Dynamical Systems . . . . . . . . . . . . . . . . . . . 9
2.2 Dynamic Textures as Dynamical Systems . . . . . . . . . . . . . . . . 12
2.2.1 Learning the LDS Parameters . . . . . . . . . . . . . . . . . . 13
2.3 A Sub-Optimal LDS Solution . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 The Solution and Algorithms . . . . . . . . . . . . . . . . . . 15
2.3.2 Analysis of the LDS modeling . . . . . . . . . . . . . . . . . . 19
2.4 A Closed-Loop Dynamical System Modeling . . . . . . . . . . . . . . 23
3 Spatio-Temporal Embeddings for Dynamic Textures 27
3.1 Time Delay Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . 29
i
3.2 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 Learning Spatio-Temporal Embeddings for Dynamic Textures . . . . 36
3.3.1 Linear Latent Model . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.2 Multiple Local Linear Models . . . . . . . . . . . . . . . . . . 39
4 Experimental Results 43
5 Conclusions 49
A EM algorithm for Linear Dynamical Systems 53
A.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
A.2 E-step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
A.3 M-step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
B Mixtures of Probabilistic Principal Component Analyzers 59
B.1 Probabilistic Principal Component Analysis . . . . . . . . . . . . . . 60
B.2 The Mixture Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
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