GRADE: Gibbs Reaction and Diffusion Equations

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GRADE: Gibbs Reaction and Diffusion Equations

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Title: GRADE: Gibbs Reaction and Diffusion Equations
Author: Mumford, David Bryant; Zhu, Song Chun

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Citation: Zhu, Song Chun, and David Bryant Mumford. 1998. GRADE: Gibbs reaction and diffusion equations. In Proceedings of the Sixth International Conference on Computer Vision: January 4 - 7, 1998, Bombay, India, ed. IEEE Computer Society, 847-854. New Delhi: Narosa.
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Abstract: Recently there have been increasing interests in using nonlinear PDEs for applications in computer vision and image processing. In this paper, we propose a general statistical framework for designing a new class of PDEs. For a given application, a Markov random field model \(p(I)\) is learned according to the minimax entropy principle so that \(p(I)\) should characterize the ensemble of images in our application. \(P(I)\) is a Gibbs distribution whose energy terms can be divided into two categories. Subsequently the partial differential equations given by gradient descent on the Gibbs potential are essentially reaction-diffusion equations, where the energy terms in one category produce anisotropic diffusion while the inverted energy terms in the second category produce reaction associated with pattern formation. We call this new class of PDEs the Gibbs Reaction And Diffusion Equations-GRADE and we demonstrate experiments where GRADE are used for texture pattern formation, denoising, image enhancement, and clutter removal.
Published Version: doi:10.1109/ICCV.1998.710816
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  • FAS Scholarly Articles [6463]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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