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Author Paget, Rupert ♦ Longstaff, Dennis
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Texture Synthesis ♦ Stochastic Type ♦ Novel Multiscale Approach ♦ Parzen Estimation ♦ High Order Statistical Property ♦ Wide Variety ♦ Large Neighbourhood System ♦ Texture Model ♦ Conditional Probability Density Function ♦ Non-parametric Markov Random Field ♦ Brodatz Album
Description In Proceedings of DICTA 95, Digital Image Computing: Techniques and Applications
In this paper we present a non-causal non-parametric multiscale Markov random field (MRF) texture model that is capable of synthesising a wide variety of textures. The textures that this model is capable of synthesising vary from the highly structured to the stochastic type and include those found in the Brodatz album of textures. The texture model uses Parzen estimation to estimate the conditional probability density function that defines the MRF. For texture synthesis we introduce a novel multiscale approach. We show that these two facets of the model give the ability to model textures requiring large neighbourhood systems to incorporate high order statistical properties of the texture.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1995-01-01