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Information Theory and Statistics
, 1968
"... Entropy and relative entropy are proposed as features extracted from symbol sequences. Firstly, a proper Iterated Function System is driven by the sequence, producing a fractaMike representation (CSR) with a low computational cost. Then, two entropic measures are applied to the CSR histogram of th ..."
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Cited by 1805 (2 self)
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Entropy and relative entropy are proposed as features extracted from symbol sequences. Firstly, a proper Iterated Function System is driven by the sequence, producing a fractaMike representation (CSR) with a low computational cost. Then, two entropic measures are applied to the CSR histogram of the CSR and theoretically justified. Examples are included.
A Survey of Shape Analysis Techniques
 Pattern Recognition
, 1998
"... This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems. ..."
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Cited by 267 (2 self)
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This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.
The induction of dynamical recognizers
 Machine Learning
, 1991
"... A higher order recurrent neural network architecture learns to recognize and generate languages after being "trained " on categorized exemplars. Studying these networks from the perspective of dynamical systems yields two interesting discoveries: First, a longitudinal examination of the le ..."
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Cited by 225 (14 self)
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A higher order recurrent neural network architecture learns to recognize and generate languages after being "trained " on categorized exemplars. Studying these networks from the perspective of dynamical systems yields two interesting discoveries: First, a longitudinal examination of the learning process illustrates a new form of mechanical inference: Induction by phase transition. A small weight adjustment causes a "bifurcation" in the limit behavior of the network. This phase transition corresponds to the onset of the network’s capacity for generalizing to arbitrarylength strings. Second, a study of the automata resulting from the acquisition of previously published training sets indicates that while the architecture is not guaranteed to find a minimal finite automaton consistent with the given exemplars, which is an NPHard problem, the architecture does appear capable of generating nonregular languages by exploiting fractal and chaotic dynamics. I end the paper with a hypothesis relating linguistic generative capacity to the behavioral regimes of nonlinear dynamical systems.
The Dynamical Hypothesis in Cognitive Science
 Behavioral and Brain Sciences
, 1997
"... The dynamical hypothesis is the claim that cognitive agents are dynamical systems. It stands opposed to the dominant computational hypothesis, the claim that cognitive agents are digital computers. This target article articulates the dynamical hypothesis and defends it as an open empirical alternati ..."
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Cited by 175 (1 self)
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The dynamical hypothesis is the claim that cognitive agents are dynamical systems. It stands opposed to the dominant computational hypothesis, the claim that cognitive agents are digital computers. This target article articulates the dynamical hypothesis and defends it as an open empirical alternative to the computational hypothesis. Carrying out these objectives requires extensive clarification of the conceptual terrain, with particular focus on the relation of dynamical systems to computers. Key words cognition, systems, dynamical systems, computers, computational systems, computability, modeling, time. Long Abstract The heart of the dominant computational approach in cognitive science is the hypothesis that cognitive agents are digital computers; the heart of the alternative dynamical approach is the hypothesis that cognitive agents are dynamical systems. This target article attempts to articulate the dynamical hypothesis and to defend it as an empirical alternative to the compu...
ReactionDiffusion Textures
 Computer Graphics
, 1991
"... We present a method for texture synthesisbased on the simulation of a process of local nonlinear interaction, called reactiondiffusion, which has been proposed as a model of biological pattern formation. We extend traditional reactiondiffusion systems by allowing anisotropic and spatially nonunif ..."
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Cited by 151 (0 self)
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We present a method for texture synthesisbased on the simulation of a process of local nonlinear interaction, called reactiondiffusion, which has been proposed as a model of biological pattern formation. We extend traditional reactiondiffusion systems by allowing anisotropic and spatially nonuniform diffusion, as well as multiple competing directions of diffusion. We adapt reactiondiffusion systems to the needs of computer graphics by presenting a method to synthesize patterns which compensate for the effects of nonuniform surface parameterization. Finally, we develop efficient algorithms for simulating reactiondiffusion systems and display a collection of resulting textures using standard texture and displacementmapping techniques. 1 Introduction Texture mapping techniques have become so highly developed and so widely used that textureless images tend to appear barren, unrealistic, and boring. To date, though, techniques for synthesizing natural textures have advanced far less...
MULTISCALE RECURSIVE ESTIMATION, DATA FUSION, AND REGULARIZATION
"... A current topic of great interest is the multiresolution analysis of signals and the development of multiscale signal processing algorithms. In this paper we describe a framework for modeling stochastic phenomena at multiple scales and for their efficient estimation or reconstruction given partial a ..."
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Cited by 136 (43 self)
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A current topic of great interest is the multiresolution analysis of signals and the development of multiscale signal processing algorithms. In this paper we describe a framework for modeling stochastic phenomena at multiple scales and for their efficient estimation or reconstruction given partial and/or noisy measurements which may also be at several scales. In particular multiscale signal representations lead naturally to pyramidal or treelike data structures in which each level in the tree corresponds to a particular scale of representaion. Noting that scale plays the role of a timelike variable, we introduce a class of multiscale dynamic models evolving on dyadic trees. The main focus of this paper is on the description, analysis, and application of an extremely efficient optimal estimation algorithm for this class of models. This algorithm consists of a finetocoarse filtering sweep, followed by a coarsetofine smoothing step, corresponding to the dyadic tree generalization of Kalman filtering and RauchTungStiebel smoothing. The Kalman filtering sweep consists of the recursive application of 3 steps: a measurement update step, a finetocoarse prediction step, and a fusion step, the latter of which has no counterpart for time (rather than scale) recursive Kalman filtering. We illustrate the use of our methodology for the fusion of multiresolution data and for
Estimating fractal dimension
 Journal of the Optical Society of America A
, 1990
"... Fractals arise from a variety of sources and have been observed in nature and on computer screens. One of the exceptional characteristics of fractals is that they can be described by a noninteger dimension. The geometry of fractals and the mathematics of fractal dimension have provided useful tools ..."
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Cited by 123 (4 self)
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Fractals arise from a variety of sources and have been observed in nature and on computer screens. One of the exceptional characteristics of fractals is that they can be described by a noninteger dimension. The geometry of fractals and the mathematics of fractal dimension have provided useful tools for a variety of scientific disciplines, among which is chaos. Chaotic dynamical systems exhibit trajectories in their phase space that converge to a strange attractor. The fractal dimension of this attractor counts the effective number of degrees of freedom in the dynamical system and thus quantifies its complexity. In recent years, numerical methods have been developed for estimating the dimension directly from the observed behavior of the physical system. The purpose of this paper is to survey briefly the kinds of fractals that appear in scientific research, to discuss the application of fractals to nonlinear dynamical systems, and finally to review more comprehensively the state of the art in numerical methods for estimating the fractal dimension of a strange attractor. Confusion is a word we have invented for an order which is not understood.Henry Miller, "Interlude," Tropic of Capricorn Numerical coincidence is a common path to intellectual perdition in our quest for meaning. We delight in catalogs of disparate items united by the same number, and often feel in our gut that some unity must underlie it all.
Agentbased computational models and generative social science
 Complexity
, 1999
"... This article argues that the agentbased computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the followi ..."
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Cited by 122 (0 self)
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This article argues that the agentbased computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the following specific contributions to social science are discussed: The agentbased computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agentbased modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agentbased (“bottom up”) models. The agentbased approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agentbased modeling offers powerful new forms of hybrid theoreticalcomputational work; these are particularly relevant to the study of nonequilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agentbased modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. � 1999 John Wiley &
Sets of matrices all infinite products of which converge. Linear Algebra and its Applications
, 1992
"... An infinite product IIT = lMi of matrices converges (on the right) if limi _ _ M,... Mi exists. A set Z = (Ai: i> l} of n X n matrices is called an RCP set (rightconvergent product set) if all infinite products with each element drawn from Z converge. Such sets of matrices arise in constructing ..."
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Cited by 114 (0 self)
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An infinite product IIT = lMi of matrices converges (on the right) if limi _ _ M,... Mi exists. A set Z = (Ai: i> l} of n X n matrices is called an RCP set (rightconvergent product set) if all infinite products with each element drawn from Z converge. Such sets of matrices arise in constructing selfsimilar objects like von Koch’s snowflake curve, in various interpolation schemes, in constructing wavelets of compact support, and in studying nonhomogeneous Markov chains. This paper gives necessary conditions and also some sufficient conditions for a set X to be an RCP set. These are conditions on the eigenvalues and left eigenspaces of matrices in 2 and finite products of these matrices. Necessary and sufficient conditions are given for a finite set Z to be an RCP set having a limit function M,(d) = rIT = lAd,, where d = (d,,., d,,..>, which is a continuous function on the space of all sequences d with the sequence topology. Finite RCP sets of columnstochastic matrices are completely characterized. Some results are given on the problem of algorithmically
Modeling and estimation of multiresolution stochastic processes
 IEEE TRANS. ON INFORMATION THEORY
, 1992
"... An overview is provided of the several components of a research effort aimed at the development of a theory of multiresolution stochastic modeling and associated techniques for optimal multiscale statistical signal and image processing. As described, a natural framework for developing such a theory ..."
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Cited by 107 (18 self)
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An overview is provided of the several components of a research effort aimed at the development of a theory of multiresolution stochastic modeling and associated techniques for optimal multiscale statistical signal and image processing. As described, a natural framework for developing such a theory is the study of stochastic processes indexed by nodes on lattices or trees in which different depths in the tree or lattice correspond to different spatial scales in representing a signal or image. In particular, it will be seen how the wavelet transform directly suggests such a modeling paradigm. This perspective then leads directly to the investigation of several classes of dynamic models and related notions of “ multiscale stationarity ” in which scale plays the role of a timelike variable. Focus is primarily on the investigation of models on homogenous trees. In particular, the elements of a dynamic system theory on trees are described