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42
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. ..."
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Cited by 478 (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 ...
A Parametric Texture Model based on Joint Statistics of Complex Wavelet Coefficients
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2000
"... We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We de ..."
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Cited by 410 (13 self)
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We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.
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 42 (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 Parametric Texture Model Based on Joint . . .
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2000
"... We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We de ..."
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Cited by 34 (0 self)
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We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.
Texture Modeling and Synthesis using Joint Statistics of Complex Wavelet Coefficients
 IN IEEE WORKSHOP ON STATISTICAL AND COMPUTATIONAL THEORIES OF VISION, FORT COLLINS
, 1999
"... We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observations of statistical regularities in such images, and parameterized by (1) the local autocorrelation of the coefficients in each ..."
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Cited by 32 (3 self)
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We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observations of statistical regularities in such images, and parameterized by (1) the local autocorrelation of the coefficients in each subband; (2) both the local autocorrelation and crosscorrelation of coefficient magnitudes at other orientations and spatial scales; and (3) the first few moments of the image pixel histogram. We develop an efficient algorithm for synthesizing random images subject to these constraints using alternated projections, and demonstrate its effectiveness on a wide range of synthetic and natural textures. In particular, we show that many important structural elements in textures (e.g., edges, repeated patterns or alternated patches of simpler texture), can be captured through joint second order statistics of the coefficient magnitudes. We also show the flexibility of the representation, by applying to a variety...
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 26 (6 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...
Waveletbased texture analysis and synthesis using hidden Markov models
 IEEE Trans. Circuits Syst. I
, 2003
"... Waveletdomain hidden Markov models (HMMs), in particular hidden Markov tree (HMT), were recently proposed and applied to image processing, where it was usually assumed that three subbands of the 2D discrete wavelet transform (DWT), i.e. HL, LH, and HH, are independent. In this paper, we study wave ..."
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Cited by 21 (2 self)
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Waveletdomain hidden Markov models (HMMs), in particular hidden Markov tree (HMT), were recently proposed and applied to image processing, where it was usually assumed that three subbands of the 2D discrete wavelet transform (DWT), i.e. HL, LH, and HH, are independent. In this paper, we study waveletbased texture analysis and synthesis using HMMs. Particularly, we develop a new HMM, called HMT3S, for statistical texture characterization in the waveletdomain. In addition to the joint statistics captured by HMT, the new HMT3S can also exploit the crosscorrelation across DWT subbands. Meanwhile, HMT3S can be characterized by using the graphical grouping technique, and has the same tree structure as HMT. The proposed HMT3S is applied to texture analysis, including classification and segmentation, and texture synthesis with improved performance over HMT. Specifically, for texture classification, we study four waveletbased methods, and experimental results show that HMT3S provides the highest percentage of correct classification of over 95 % upon a set of 55 Brodatz textures. For texture segmentation, we demonstrate that more accurate texture characterization from HMT3S allows the significant improvements in terms of both classification accuracy and boundary localization. For texture synthesis, we develop an iterative
Texture SynthesisByAnalysis Based On A Multiscale Earlyvision Model
, 1996
"... This paper introduces a new texture synthesisbyanalysis method, applying a visualbased approach which has some important advantages over more traditional texture modeling and synthesis techniques. The basis of the method is to encode the textural information by sampling both the power spectrum an ..."
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Cited by 17 (4 self)
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This paper introduces a new texture synthesisbyanalysis method, applying a visualbased approach which has some important advantages over more traditional texture modeling and synthesis techniques. The basis of the method is to encode the textural information by sampling both the power spectrum and the histogram of homogeneously textured images. The spectrum is sampled in a logpolar grid by using a pyramid Gabor scheme. The input image is split into a set of 16 Gabor channels (using four spatial frequency levels and four orientations), plus a lowpass residual (LPR). The energy and equivalent bandwidths of each channel, as well as the LPR power spectrum and the histogram, are measured and the latter two are compressed. The synthesis process consists of generating 16 Gabor filtered independent noise signals with spectral centers equal to those of the Gabor filters, whose energy and equivalent bandwidths are calculated in order to reproduce the measured values. These bandpass signals...
Texture Analysis through a Markovian Modelling and Fuzzy Classification: Application to Urban Area Extraction from Satellite Images
, 1998
"... Abstract. Herein we propose a complete procedure to analyze and classify the texture of an image. We apply this scheme to solve a specific image processing problem: urban areas detection in satellite images. First we propose to analyze the texture through the modelling of the luminance field with ei ..."
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Cited by 15 (5 self)
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Abstract. Herein we propose a complete procedure to analyze and classify the texture of an image. We apply this scheme to solve a specific image processing problem: urban areas detection in satellite images. First we propose to analyze the texture through the modelling of the luminance field with eight different chainbased models. We then derived a texture parameter from these models. The effect of the lattice anisotropy is corrected by a renormalization group technique coming from statistical physics. This parameter, which takes into account local conditional variances of the image, is compared to classical methods of texture analysis. Afterwards we develop a modified fuzzy Cmeans algorithm that includes an entropy term. The advantage of such an algorithm is that the number of classes does not need to be known a priori. Besides this algorithm provides us with further information, i.e. the probability that a given pixel belongs to a given cluster. Finally we introduce this information in a Markovian model of segmentation. Some results on SPOT5 simulated images, SPOT3 images and ERS1 radar images are presented. These images are provided by the French National Space Agency (CNES) and the European Space Agency (ESA).
Texture Representation and Synthesis Using Correlation of Complex Wavelet Coefficient Magnitudes
 Tech. Rep. 54, Consejo Superior de Investigaciones Cientificas (CSIC
, 1999
"... We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observations of statistical regularities in such images, and parameterized by (1) the local autocorrelation of the coefficients in each ..."
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Cited by 15 (3 self)
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We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observations of statistical regularities in such images, and parameterized by (1) the local autocorrelation of the coefficients in each subband; (2) both the local autocorrelation and crosscorrelation of coefficient magnitudes at other orientations and spatial scales; and (3) the first few moments of the image pixel histogram. We develop an efficient algorithm for synthesizing random images subject to these constraints using alternated projections, and demonstrate its effectiveness on a wide range of synthetic and natural textures. We also show the flexibility of the representation, by applying to a variety of tasks which can be viewed as constrained image synthesis problems. Vision is arguably our most important sensory system, judging from both the ubiquity of visual forms of communication, and the large proportion of ...