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28
PyramidBased Texture Analysis/Synthesis
, 1995
"... This paper describes a method for synthesizing images that match the texture appearanceof a given digitized sample. This synthesis is completely automatic and requires only the "target" texture as input. It allows generation of as much texture as desired so that any object can be covered. It can be ..."
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Cited by 385 (0 self)
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This paper describes a method for synthesizing images that match the texture appearanceof a given digitized sample. This synthesis is completely automatic and requires only the "target" texture as input. It allows generation of as much texture as desired so that any object can be covered. It can be used to produce solid textures for creating textured 3d objects without the distortions inherent in texture mapping. It can also be used to synthesize texture mixtures, images that look a bit like each of several digitized samples. The approach is based on a model of human texture perception, and has potential to be a practically useful tool for graphics applications. 1 Introduction Computer renderings of objects with surface texture are more interesting and realistic than those without texture. Texture mapping [15] is a technique for adding the appearance of surface detail by wrapping or projecting a digitized texture image ontoa surface. Digitized textures can be obtained from a variety ...
On Conditional and Intrinsic Autoregressions
, 1995
"... This paper discusses standard and intrinsic autoregressions and describes how the problems that arise can be alleviated using Dempster's (1972) algorithm or an appropriate modification. The approach partly represents a synthesis of standard geostatistical and Gaussian Markov random field formulation ..."
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Cited by 75 (6 self)
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This paper discusses standard and intrinsic autoregressions and describes how the problems that arise can be alleviated using Dempster's (1972) algorithm or an appropriate modification. The approach partly represents a synthesis of standard geostatistical and Gaussian Markov random field formulations. Some nonspatial applications are also mentioned. Some key words: Agricultural experiments; Bayesian image analysis; Conditional autoregressions; Dempster's algorithm; Geographical epidemiology; Geostatistics; Intrinsic autoregressions; Multiway tables; Prior distributions; Spatial statistics; Surface reconstruction; Texture analysis. 1 Introduction
Novel ClusterBased Probability Model for Texture Synthesis, Classification, and Compression
 In Visual Communications and Image Processing
, 1993
"... We present a new probabilistic modeling technique for highdimensional vector sources, and consider its application to the problems of texture synthesis, classification, and compression. Our model combines kernel estimation with clustering, to obtain a semiparametric probability mass function estima ..."
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Cited by 68 (6 self)
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We present a new probabilistic modeling technique for highdimensional vector sources, and consider its application to the problems of texture synthesis, classification, and compression. Our model combines kernel estimation with clustering, to obtain a semiparametric probability mass function estimate which summarizes  rather than contains  the training data. Because the model is cluster based, it is inferable from a limited set of training data, despite the model's high dimensionality. Moreover, its functional form allows recursive implementation that avoids exponential growth in required memory as the number of dimensions increases. Experimental results are presented for each of the three applications considered. 1. INTRODUCTION In many information processing tasks individual data samples exhibit a great deal of statistical interdependence, and should be treated jointly (e.g., in vectors) rather than separately. For some tasks this requires modeling vectors probabilistically....
A NonHierarchical Procedure for ReSynthesis of Complex Textures
 In WSCG ’2001 Conference proceedings
, 2001
"... A procedure is described for synthesizing an image with the same texture as a given input image. ..."
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Cited by 48 (0 self)
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A procedure is described for synthesizing an image with the same texture as a given input image.
Generalized Stochastic Subdivision
 ACM Transactions on Graphics
, 1987
"... This paper describes the basis for techniques such as stochastic subdivision in the theory of random processes and estimation theory. The popular stochastic subdivision construction is then generalized to provide control of the autocorrelation and spectral properties of the synthesized random functi ..."
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Cited by 37 (2 self)
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This paper describes the basis for techniques such as stochastic subdivision in the theory of random processes and estimation theory. The popular stochastic subdivision construction is then generalized to provide control of the autocorrelation and spectral properties of the synthesized random functions. The generalized construction is suitable for generating a variety of perceptually distinct highquality random functions, including those with nonfractal spectra and directional or oscillatory characteristics. It is argued that a spectral modeling approach provides a more powerful and somewhat more intuitive perceptual characterization of random processes than does the fractal model. Synthetic textures and terrains are presented as a means of visually evaluating the generalized subdivision technique. Categories and Subject Descriptors: I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism <F11.
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
 IEEE TRANS. ON PATTERN ANAL. AND MACHINE INTELLIGENCE
, 2002
"... In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant vari ..."
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Cited by 33 (3 self)
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In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 12 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.
A Twocomponent Model of Texture for Analysis and Synthesis
, 1998
"... A model of natural texture based on a structural component which uses affine coordinate transformations and a stochastic residual component is presented. It is argued that the selection of an appropriate analysis scale can be formulated in terms of a tradeoff between the rate at which parameters ..."
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Cited by 21 (5 self)
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A model of natural texture based on a structural component which uses affine coordinate transformations and a stochastic residual component is presented. It is argued that the selection of an appropriate analysis scale can be formulated in terms of a tradeoff between the rate at which parameters are generated and the distortion resulting from the approximation by the structural component. An efficient algorithm for identifying the parameters of the structural model is described and its utility demonstrated on a number of synthetic and natural textures. 1 Introduction The analysis and synthesis of natural textures is one of the most intriguing and difficult problems in image processing, which has received much attention over the years [2, 20, 26, 21, 16, 10, 34, 35, 5, 4, 17]. Apart from their significance in applications ranging from remote sensing to computer graphics, the analysis and synthesis of texture have remained challenging problems because of the combination of stru...
Wavelet Image Extension for Analysis and Classification of Infarcted Myocardial Tissue
 IEEE Trans. Biomedical Engineering
, 1997
"... Some computer applications for tissue characterization in medicine and biology, such as analysis of the myocardium or cancer recognition, operate with tissue samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture opera ..."
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Cited by 8 (1 self)
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Some computer applications for tissue characterization in medicine and biology, such as analysis of the myocardium or cancer recognition, operate with tissue samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and yet be reliable in order to estimate texture quality from the small number of image points available. In order to describe the quality of infarcted myocardial tissue, we propose a new waveletbased approach for analysis and classification of texture samples with small dimensions. The main idea of this method is to decompose the given image with a filter bank derived from an orthonormal wavelet basis and to form an image approximation with higher resolution. Texture energy measures calculated at each output of the filter bank as well as energies of synthesized images are used as texture features in a classification procedure. We propose an unsupervised classification technique based on a modified statistical ttest.
Scalable Data Parallel Algorithms for Texture Synthesis using Gibbs Random Fields
 University of Maryland, College Park, MD
, 1993
"... This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, wepresent parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinki ..."
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Cited by 7 (2 self)
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This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, wepresent parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM2 and CM5. Use of #negrained, data parallel processing techniques yields realtime algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented here enables machine independent scalable algorithms for a number of problems in image processing and analysis.
Bayesian Segmentation via Asymptotic Partition Functions
, 2000
"... Asymptotic approximations to the partition function of Gaussian random fields are derived. Textures are characterized via Gaussian random fields induced by stochastic di#erence equations determined by finitely supported, stationary, linear di#erence operators, adjusted to be nonstationary at the bo ..."
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Cited by 4 (0 self)
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Asymptotic approximations to the partition function of Gaussian random fields are derived. Textures are characterized via Gaussian random fields induced by stochastic di#erence equations determined by finitely supported, stationary, linear di#erence operators, adjusted to be nonstationary at the boundaries. It is shown that as the scale of the underlying shape increases, the lognormalizer converges to the integral of the logspectrum of the operator inducing the random field. Fitting the covariance of the fields amounts to fitting the parameters of the spectrum of the di#erential operatorinduced random field model. Matrix analysis techniques are proposed for handling textures with variable orientation. Examples of texture parameters estimated from training data via asymptotic maximumlikelihood are shown. Isotropic models involving powers of the Laplacian and directional models involving partial derivative mixtures are explored. Parameters are estimated for mitochondria and actinm...