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24
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 ...
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 Compact Model for Viewpoint Dependent Texture Synthesis
 of Lecture Notes in Computer Science
, 2001
"... A texture synthesis method is presented that generates similar texture from an example image. It is based on the emulation of simple but rather carefully chosen image intensity statistics. The resulting texture models are compact and no longer require the example image from which they were deriv ..."
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Cited by 30 (6 self)
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A texture synthesis method is presented that generates similar texture from an example image. It is based on the emulation of simple but rather carefully chosen image intensity statistics. The resulting texture models are compact and no longer require the example image from which they were derived. They make explicit some structural aspects of the textures and the modeling allows knitting together different textures with convincingly looking transition zones. As textures are seldom flat, it is important to also model 3D effects when textures change under changing viewpoint. The simulation of such changes is supported by the model, assuming examples for the different viewpoints are given.
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 22 (2 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...
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 12 (3 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 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 12 (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 ...
Texture Synthesis Using GrayLevel CoOccurrence Models: Algorithms, Experimental Analysis, and Psychophysical Support
, 2001
"... The development and evaluation of texture synthesis algorithms is discussed. We present texture synthesis algorithms based on the graylevel cooccurrence (GLC) model of a texture field. These algorithms use a texture similarity metric, which is shown to have high correlation with human perception o ..."
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Cited by 10 (0 self)
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The development and evaluation of texture synthesis algorithms is discussed. We present texture synthesis algorithms based on the graylevel cooccurrence (GLC) model of a texture field. These algorithms use a texture similarity metric, which is shown to have high correlation with human perception of textures. Synthesis algorithms are evaluated using extensive experimental analysis. These experiments are designed to compare various iterative algorithms for synthesizing a random texture possessing a given set of secondorder probabilities as characterized by a GLC model. Three texture test cases are selected to serve as the targets for the synthesis process in the experiments. The three texture test cases are selected so as to represent three different types of primitive texture: disordered, weakly ordered, and strongly ordered. For each experiment, we judge the relative quality of the algorithms by two criteria. First, we consider the quality of the final synthesized result in terms of the visual similarity to the target texture as well as a numerical measure of the error between the GLC models of the synthesized texture and the target texture. Second, we consider the relative computational efficiency of an algorithm, in terms of how quickly the algorithm converges to the final result. We conclude that a multiresolution version of the "spin flip" algorithm, where an individual pixel's gray level is changed to the gray level that most reduces the weighted error between the images second order probabilities and the target probabilities, performs the best for all of the texture test cases considered. Finally, with the help of psychophysical experiments, we demonstrate that the results for the texture synthesis algorithms have high correlation with the texture similaritie...
Deterministic texture analysis and synthesis using tree structure vector quantization
 In XII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPHI
, 1999
"... Abstract. Texture analysis and synthesis is very important for computer graphics, vision, and image processing. This paper describes an algorithm which can produce new textures with a matching visual appearance from a given example image. Our algorithm is based on a model that characterizes textures ..."
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Cited by 9 (2 self)
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Abstract. Texture analysis and synthesis is very important for computer graphics, vision, and image processing. This paper describes an algorithm which can produce new textures with a matching visual appearance from a given example image. Our algorithm is based on a model that characterizes textures using a nonlinear deterministic function. During analysis, an example texture is summarized into this function using tree structure vector quantization. An output texture, initially random noise, is then synthesized from this estimated function. Compared to existing approaches, our algorithm can efficiently generate a wide variety of textures. The effectiveness of our approach is demonstrated using standard test images from the Brodatz texture album.