Probabilistic image modeling via neural networks

Authors:
Jenq-Neng Hwang;Eric T. Y. Chen;Alan F. Lippman
Affiliations:
Information Processing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA;Information Processing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA;Information Processing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

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Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

This paper proposes a neural network approach for the estimation of the local conditional distributions of textured images. We then use these distributions to generate a probability distribution on the entire image. The proposed approach overcomes many of the difficulties encountered when using Markov random field (MRF) approaches. In particular our approach does not require the trial-and-error choice of clique functions or the subsequent estimation of clique parameters. Simulations show that the images synthesized using neural network modeling produced desired textures more consistently than MRF/Gibbs distribution based methods.