Results 1 
9 of
9
Texture Synthesis over Arbitrary Manifold Surfaces
, 2001
"... Algorithms exist for synthesizing a wide variety of textures over rectangular domains. However, it remains difficult to synthesize general textures over arbitrary manifold surfaces. In this paper, we present a solution to this problem for surfaces defined by dense polygon meshes. Our solution extend ..."
Abstract

Cited by 137 (8 self)
 Add to MetaCart
Algorithms exist for synthesizing a wide variety of textures over rectangular domains. However, it remains difficult to synthesize general textures over arbitrary manifold surfaces. In this paper, we present a solution to this problem for surfaces defined by dense polygon meshes. Our solution extends Wei and Levoy's texture synthesis method [25] by generalizing their definition of search neighborhoods. For each mesh vertex, we establish a local parameterization surrounding the vertex, use this parameterization to create a small rectangular neighborhood with the vertex at its center, and search a sample texture for similar neighborhoods. Our algorithm requires as input only a sample texture and a target model. Notably, it does not require specification of a global tangent vector field; it computes one as it goes  either randomly or via a relaxation process. Despite this, the synthesized texture contains no discontinuities, exhibits low distortion, and is perceived to be similar to the sample texture. We demonstrate that our solution is robust and is applicable to a wide range of textures. Keywords: Texture Synthesis, Texture Mapping, Curves & Surfaces 1
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 ..."
Abstract

Cited by 37 (2 self)
 Add to MetaCart
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.
Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo  Towards a "Trichromacy" Theory of Texture
, 1999
"... This article presents a mathematical denition of texture { the Julesz ensemble h), which is the set of all images (defined on Z²) that share identical statistics h. Then texture modeling is posed as an inverse problem: given a set of images sampled from an unknown Julesz ensemble h ), we search f ..."
Abstract

Cited by 32 (13 self)
 Add to MetaCart
This article presents a mathematical denition of texture { the Julesz ensemble h), which is the set of all images (defined on Z²) that share identical statistics h. Then texture modeling is posed as an inverse problem: given a set of images sampled from an unknown Julesz ensemble h ), we search for the statistics h which define the ensemble. A Julesz ensemble h) has an associated probability distribution q(I; h), which is uniform over the images in the ensemble and has zero probability outside. In a companion paper [32], q(I; h) is shown to be the limit distribution of the FRAME (Filter, Random Field, And Minimax Entropy) model[35] as the image lattice ! Z². This conclusion establishes the intrinsic link between the scientific definition of texture on Z² and the mathematical models of texture on finite lattices. It brings two advantages to computer vision. 1). The engineering practice of synthesizing texture images by matching statistics has been put on a mathematical fou...
Equivalence of Julesz Ensembles and FRAME Models
 International Journal of Computer Vision
, 2000
"... In the past thirty years, research on textures has been pursued along two different lines. The first line of research, pioneered by Julesz (1962), seeks essential ingredients in terms of features and statistics in human texture perception. This leads us to a mathematical definition of textures in te ..."
Abstract

Cited by 19 (6 self)
 Add to MetaCart
In the past thirty years, research on textures has been pursued along two different lines. The first line of research, pioneered by Julesz (1962), seeks essential ingredients in terms of features and statistics in human texture perception. This leads us to a mathematical definition of textures in terms of Julesz ensembles [26]. A Julesz ensemble is a set of images that share the same value of some basic feature statistics. Images in the Julesz ensemble are defined on a large image lattice (a mathematical idealization being Z²) so that exact constraint on feature statistics makes sense. The second line of research studies Markov random field (MRF) models that characterize texture patterns on finite (or small) image lattice in a statistical way. This leads us to a general class of MRF models called FRAME (Filter, Random field, And Maximum Entropy) [27]. In this article, we bridge the two lines of research by the fundamental principle of equivalence of ensembles in statistical mechanics (Gibbs...
Towards Local Control for ImageBased Texture Synthesis
 In: Proc. of XV Brazilian Symposium on Computer Graphics and Image Processing
, 2002
"... New advances in image based texture synthesis techniques allow the generation of arbitrarily sized textures based on a small sample. The generated textures are perceived as very similar to the given sample. One main drawback of these techniques, however, is that the synthesized result cannot be lo ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
New advances in image based texture synthesis techniques allow the generation of arbitrarily sized textures based on a small sample. The generated textures are perceived as very similar to the given sample. One main drawback of these techniques, however, is that the synthesized result cannot be locally controlled, that is, we are able to synthesize a larger version of the sample but without much variation. We present in this paper a technique which improves on current fast texture synthesis techniques by allowing local control over the result.
An Algorithm for Automated Fractal Terrain Deformation
 In Proceedings of Computer Graphics and Artificial Intelligence
, 2005
"... www.cs.yorku.ca/~wolfgang/ Fractal terrains provide an easy way to generate realistic landscapes. There are several methods to generate fractal terrains, but none of those algorithms allow the user much flexibility in controlling the shape or properties of the final outcome. A few methods to modify ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
www.cs.yorku.ca/~wolfgang/ Fractal terrains provide an easy way to generate realistic landscapes. There are several methods to generate fractal terrains, but none of those algorithms allow the user much flexibility in controlling the shape or properties of the final outcome. A few methods to modify fractal terrains have been previously proposed, both algorithmbased as well as by hand editing, but none of these provide a general solution. In this work, we present a new algorithm for fractal terrain deformation. We present a general solution that can be applied to a wide variety of deformations. Our approach employs stochastic local search to identify a sequence of local modifications, which deform the fractal terrain to conform to a set of specified constraints. The presented results show that the new method can incorporate multiple constraints simultaneously, while still preserving the natural look of the fractal terrain. Keywords: (according to ACM CCS): I.3.7 [Computer Graphics, ThreeDimensional Graphics and Realism]: Fractals, I.2.8 [Problem Solving, Control Methods, and Search] Graph and tree search strategies
State of the Art in Procedural Noise Functions
, 2010
"... ProceduralnoisefunctionsarewidelyusedinComputerGraphics, from offlinerenderinginmovieproductionto interactivevideogames.Theabilitytoaddcomplexandintricate detailsatlowmemoryandauthoringcostisone of its main attractions. This stateoftheart report is motivated by the inherent importance of noise i ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
ProceduralnoisefunctionsarewidelyusedinComputerGraphics, from offlinerenderinginmovieproductionto interactivevideogames.Theabilitytoaddcomplexandintricate detailsatlowmemoryandauthoringcostisone of its main attractions. This stateoftheart report is motivated by the inherent importance of noise in graphics, thewidespreaduseofnoiseinindustry,andthefactthatmany recentresearchdevelopmentsjustifytheneedforan uptodatesurvey.Ourgoalistoprovidebothavaluableentrypointinto thefieldofproceduralnoisefunctions,as wellasacomprehensiveviewofthefieldtotheinformedreader. Inthisreport,wecoverproceduralnoisefunctions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied on surfaces. We then introduce analysis tools and apply them to evaluate andcompare the major approaches tonoisegeneration. We finally identify several directions for future work.
Morphing Textures with Texton Masks
"... Image morphing has been extensively studied in computer graphics. Given two input images, morphing algorithms produce a sequence of inbetween images which transforms the source image into the target image in a visually pleasant way. In this paper, we propose an algorithm, based on recent advances fr ..."
Abstract
 Add to MetaCart
Image morphing has been extensively studied in computer graphics. Given two input images, morphing algorithms produce a sequence of inbetween images which transforms the source image into the target image in a visually pleasant way. In this paper, we propose an algorithm, based on recent advances from texturefromsample ideas, which synthesizes a morphing sequence targeted specifically for textures. We use the idea of binary masks (or texton masks) to control the morphing sequence and guarantee a coherent transition from the source texture to the target texture. 1
Volume0(1981),Number 0pp. 1–20 COMPUTER GRAPHICS forum
"... ProceduralnoisefunctionsarewidelyusedinComputerGraphics,fromofflinerenderinginmovieproductionto interactivevideogames.Theabilitytoaddcomplexandintricatedetailsatlowmemoryandauthoringcostisone of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the wide ..."
Abstract
 Add to MetaCart
ProceduralnoisefunctionsarewidelyusedinComputerGraphics,fromofflinerenderinginmovieproductionto interactivevideogames.Theabilitytoaddcomplexandintricatedetailsatlowmemoryandauthoringcostisone of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an uptodate survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare themajor approaches tonoise generation. Wefinally identify severaldirections for futurework.