Visual reconstruction
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
GRADE: Gibbs Reaction and Diffusion Equitions
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
M-lattice: from morphogenesis to image processing
IEEE Transactions on Image Processing
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A system of coupled differential equations is formulated which learns priors for modelling "preattentive" textures. It is derived from an energy functional consisting of a linear combination of a large number of terms corresponding to the features that the system is capable of learning. The system learns the parameters associated with each feature by applying gradient ascent to the log-likelihood function. Update of each parameter is thus governed by the residual with respect to the corresponding feature. A feature residual is computed from its observed value and value generated by the system. The latter is calculated from a synthesized sample image which is generated by means of a reaction-diffusion equation obtained by applying gradient descent to the energy functional.