Results 11  20
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952
Temporal Texture Modeling
 In IEEE International Conference on Image Processing
, 1996
"... Temporal textures are textures with motion. Examples include wavy water, rising steam and fire. We model image sequences of temporal textures using the spatiotemporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and ..."
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Cited by 118 (1 self)
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Temporal textures are textures with motion. Examples include wavy water, rising steam and fire. We model image sequences of temporal textures using the spatiotemporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time. The model provides a base for both recognition and synthesis. We show how the least squares method can accurately estimate model parameters for large, causal neighborhoods with more than 1000 parameters. Synthesis results show that the model can adequately capture the spatial and temporal characteristics of many temporal textures. A 95% recognition rate is achieved for a 135 element database with 15 texture classes. 1.
Deformotion  Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images
 International Journal of Computer Vision
, 2002
"... What does it mean for a deforming object to be "moving" (see Fig. 1)? How can we separate the overall motion (a finitedimensional group action) from the more general deformation (a di#eomorphism)? In this paper we propose a definition of motion for a deforming object and introduce a notion of "shap ..."
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Cited by 105 (16 self)
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What does it mean for a deforming object to be "moving" (see Fig. 1)? How can we separate the overall motion (a finitedimensional group action) from the more general deformation (a di#eomorphism)? In this paper we propose a definition of motion for a deforming object and introduce a notion of "shape average" as the entity that separates the motion from the deformation. Our definition allows us to derive novel and e#cient algorithms to register nonequivalent shapes using regionbased methods, and to simultaneously approximate and register structures in greyscale images. We also extend the notion of shape average to that of a "moving average" in order to track moving and deforming objects through time.
N4SID: Subspace Algorithms for the Identification of Combined DeterministicStochastic Systems
, 1994
"... Recently a great deal of attention has been given to numerical algorithms for subspace state space system identification (N4SID). In this paper, we derive two new N4SID algorithms to identify mixed deterministicstochastic systems. Both algorithms determine state sequences through the projection of ..."
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Cited by 103 (12 self)
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Recently a great deal of attention has been given to numerical algorithms for subspace state space system identification (N4SID). In this paper, we derive two new N4SID algorithms to identify mixed deterministicstochastic systems. Both algorithms determine state sequences through the projection of input and output data. These state sequences are shown to be outputs of nonsteady state Kalman filter banks. From these it is easy to determine the state space system matrices. The N4SID algorithms are always convergent (noniterative) and numerically stable since they only make use of QR and Singular Value Decompositions. Both N4SID algorithms are similar, but the second one trades off accuracy for simplicity. These new algorithms are compared with existing subspace algorithms in theory and in practice. Key words : Subspace identification, nonsteady state Kalman filter, Riccati difference equations, QR and Singular Value Decomposition 1 Introduction The greater part of the systems ide...
Dynamic Texture Recognition
, 2001
"... Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is no ..."
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Cited by 87 (6 self)
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Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is nonlinear, a distance between models must be defined. We examine three different distances in the space of autoregressive models and assess their power. 1.
From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks
 Proc. IEEE
, 2002
"... Mathematical and computational modeling of genetic regulatory networks promises to uncover the fundamental principles governing biological systems in an integrarive and holistic manner. It also paves the way toward the development of systematic approaches for effective therapeutic intervention in di ..."
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Cited by 84 (17 self)
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Mathematical and computational modeling of genetic regulatory networks promises to uncover the fundamental principles governing biological systems in an integrarive and holistic manner. It also paves the way toward the development of systematic approaches for effective therapeutic intervention in disease. The central theme in this paper is the Boolean formalism as a building block for modeling complex, largescale, and dynamical networks of genetic interactions. We discuss the goals of modeling genetic networks as well as the data requirements. The Boolean formalism is justified from several points of view. We then introduce Boolean networks and discuss their relationships to nonlinear digital filters. The role of Boolean networks in understanding cell differentiation and cellular functional states is discussed. The inference of Boolean networks from real gene expression data is considered from the viewpoints of computational learning theory and nonlinear signal processing, touching on computational complexity of learning and robustness. Then, a discussion of the need to handle uncertainty in a probabilistic framework is presented, leading to an introduction of probabilistic Boolean networks and their relationships to Markov chains. Methods for quantifying the influence of genes on other genes are presented. The general question of the potential effect of individual genes on the global dynamical network behavior is considered using stochastic perturbation analysis. This discussion then leads into the problem of target identification for therapeutic intervention via the development of several computational tools based on firstpassage times in Markov chains. Examples from biology are presented throughout the paper. 1
Learning Maps for Indoor Mobile Robot Navigation
 ARTIFICIAL INTELLIGENCE (ACCEPTED FOR PUBLICATION)
, 1997
"... Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: gridbased and topological. While gridbased methods produce accurate metric maps, their complexity often prohibits ..."
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Cited by 83 (12 self)
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Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: gridbased and topological. While gridbased methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in largescale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in largescale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: gridbased and topological. Gridbased maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the gridbased maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multiroom environments.
Multichannel Blind Identification: From Subspace to Maximum Likelihood Methods
 Proc. IEEE
, 1998
"... this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified ..."
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Cited by 79 (2 self)
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this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified into the momentbased and the maximum likelihood (ML) methods. We further divide these algorithms based on the modeling of the input signal. If input is assumed to be random with prescribed statistics (or distributions), the corresponding blind channel estimation schemes are considered to be statistical. On the other hand, if the source does not have a statistical description, or although the source is random but the statistical properties of the source are not exploited, the corresponding estimation algorithms are classified as deterministic. Fig. 2 shows a map for different classes of algorithms and the organization of the paper.
Subspace Algorithms for the Stochastic Identification Problem
, 1993
"... In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semiinfinite block Hankel matrices. The algorithm is based on the concept of principal angles and directions. We descr ..."
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Cited by 77 (14 self)
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In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semiinfinite block Hankel matrices. The algorithm is based on the concept of principal angles and directions. We describe how they can be calculated with QR and Quotient Singular Value Decomposition. We also provide an interpretation of the principal directions as states of a nonsteady state Kalman filter bank. Key Words Principal angles and directions, QR and quotient singular value decomposition, Kalman filter, Riccati difference equation, stochastic balancing, stochastic realization. 1 Introduction Let y k 2 ! l ; k = 0; 1; : : : ; K be a data sequence that is generated by the following system : x k+1 = Ax k + w k (1) y k = Cx k + v k (2) where x k 2 ! n is a state vector. The vector sequence w k 2 ! n is a process noise while the vector sequence v k 2 ! l is a measurement noise. They are bo...
Perspectives on system identification
 In Plenary talk at the proceedings of the 17th IFAC World Congress, Seoul, South Korea
, 2008
"... System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous ne ..."
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Cited by 77 (2 self)
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System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.
Learning and classification of complex dynamics
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... AbstractÐStandard, exact techniques based on likelihood maximization are available for learning AutoRegressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via ..."
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Cited by 76 (1 self)
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AbstractÐStandard, exact techniques based on likelihood maximization are available for learning AutoRegressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via ªEMKºÐExpectationMaximization (EM) based on Kalman Filtering. This cannot handle more complex dynamics, however, involving multiple classes of motion. A problem arises also in the case of dynamical processes observed visually: background clutter arising for example, in camouflage, produces nonGaussian observation noise. Even with a single dynamical class, nonGaussian observations put the learning problem beyond the scope of EMK. For those cases, we show here how ªEMCºÐbased on the CONDENSATION algorithm which propagates random ªparticlesets,º can solve the learning problem. Here, learning in clutter is studied experimentally using visual observations of a hand moving over a desktop. The resulting learned dynamical model is shown to have considerable predictive value: When used as a prior for estimation of motion, the burden of computation in visual observation is significantly reduced. Multiclass dynamics are studied via visually observed juggling; plausible dynamical models have been found to emerge from the learning process, and accurate classification of motion has resulted. In practice, EMC learning is computationally burdensome and the paper concludes with some discussion of computational complexity. Index TermsÐComputer vision, learning dynamics, AutoRegressive Process, Expectation Maximization. 1