Texture synthesis and nonparametric resampling of random fields
نویسندگان
چکیده
منابع مشابه
Texture Synthesis and Nonparametric Resampling of Random Fields by Elizaveta Levina
This paper introduces a nonparametric algorithm for bootstrapping a stationary random field and proves certain consistency properties of the algorithm for the case of mixing random fields. The motivation for this paper comes from relating a heuristic texture synthesis algorithm popular in computer vision to general nonparametric bootstrapping of stationary random fields. We give a formal resamp...
متن کاملTexture Classification Using Nonparametric Markov Random Fields
We present a nonparametric Markov Random Field model for classifying texture in images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this...
متن کاملLocal resampling for patch-based texture synthesis in vector fields
We develop a direct and accurate approach for local resampling in vector fields, and then use the approach to synthesise textures on 2D manifold surfaces directly from a texture exemplar. Regular-grid patches produced by the local resampling are used as building blocks for texture synthesis. Then, texture optimisation and patch-based sampling are generalised to synthesise texture directly in ve...
متن کاملTexture synthesis via a noncausal nonparametric multiscale Markov random field
Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.
متن کاملMarkov Random Fields for Super-resolution and Texture Synthesis
Suppose we want to digitally enlarge a photograph. The input is a single, low-resolution image, and the desired output is an estimate of the high-resolution version of that image. This problem can be phrased as one of “image interpolation”: we seek to interpolate the pixel values between our observed samples. Image interpolation is sometimes called super-resolution, since we are estimating data...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2006
ISSN: 0090-5364
DOI: 10.1214/009053606000000588