Some Experiments with Mosaic Models for Images (original) (raw)
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1981
This paper deals with a class of image models based on random geometric processes. Theoretical and empirical results on properties of patterns generated using these models are summarized. These properties can be used as aids in fitting the models to images.
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1979
This paper deals with a class of image models based on random geometric processes. Theoretical and empirical results on properties of patterns generated using these models are summarized. These properties can be used as aids in fitting the models to images.
On Tradeoffs Between Deterministic Structure and Randomness in Texture Simulation
Abstract The paper discusses today's techniques of simulating realistic images of natural textures. These techniques account for deterministic spatial structures of signal relationships in a given training sample and allow for random deviations of signals in the simulated texture. When simulation is based on random permutations of image tiles, these latter can be found for some periodic regular textures by using characteristic pixel neighbourhoods.
Imitation of binary random textures on the basis of Gaussian numerical models
We present a method for binary texture synthesis based on thresh-olds of Gaussian random fields. The method enables us to reproduce the average value and correlation function of the observed texture. The method is comparatively simple, and it seems to be effective for a wide class of random binary textures. In the paper we discuss prop-erties of the method and illustrate its performance in the statistically homogeneous and isotropic case.
The use of Markov Random Fields as models of texture
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Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries'' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.
The Visual Computer, 2005
Art often provides valuable insight that can be applied to technological innovations, especially in the fields of image processing and computer graphics. In this paper we present a method to transform a raster input image into a good-quality mosaic: an “artificial mosaic.” The creation of mosaics of artistic quality is challenging because the tiles that compose a mosaic, typically small polygons, must be packed tightly and yet must follow and emphasize orientations chosen by the artist. The proposed method can reproduce the colors of the original image and emphasize relevant boundaries by placing tiles along edge directions. No user intervention is needed to detect the boundaries: they are automatically detected using a simple but effective image processing technique. Several examples reported in the paper show how the right mixture of mathematical tools together with time-tested ideas of mosaicists may lead to impressive results.