Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Computer and Robot Vision
From High Energy Physics to Low Level Vision
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Images as embedding maps and minimal surfaces: movies, color, and volumetric medical images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The design of two-dimensional gradient estimators based on one-dimensional operators
IEEE Transactions on Image Processing
A general framework for low level vision
IEEE Transactions on Image Processing
Using Beltrami Framework for Orientation Diffusion in Image Processing
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
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We develop on estimation method, for the derivative field of an image based on Bayesian approach which is formulated in a geometric way. The Maximum probability configuration of the derivative field is found by a gradient descent method which leads to a non-linear diffusion type equation with added constraints. The derivatives are assumed to be piecewise smoothe and the Beltrami framework is used in the development of an adaptive smoothing process.