Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Image Analysis Using Multigrid Relaxation Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Initial Value Problems in Ordinary Differential Equations
Numerical Initial Value Problems in Ordinary Differential Equations
Robot Vision
A Fast Gibbs Sampler for Synthesizing Constrained Fractals
IEEE Transactions on Visualization and Computer Graphics
Elastically Adaptive Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coupled PDE Model of Nonlinear Diffusion for Image Smoothing and Segmentation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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The deformable sheet, a physical model that provides a natural framework for addressing many vision problems that can be solved by smoothness-constrained optimization, is described. Deformable sheets are characterized by a global energy functional, and the smoothness constraint is represented by a linear internal energy term. Analogous to physical sheets, the model sheets are deformed by problem-specific external forces and, in turn, impose smoothness on the applied forces. The model unifies the properties of scale and smoothness into a single parameter that makes it possible to perform scale space tracking by properly controlling the smoothness constraint. Specifically, the desired scale space trajectory is found by solving a differential equation in scale. The simple analytic dependence on scale also provides a mechanism for adaptive step size control. Results from application of the deformable sheet model to various problems in computational vision are presented.