Visual reconstruction
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range image restoration using mean field annealing
Advances in neural information processing systems 1
Biased anisotropic diffusion: a unified regularization and diffusion approach to edge detection
Image and Vision Computing - Special issue on the first ECCV 1990
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
Smart Nonlinear Diffusion: A Probabilistic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Higher-Order Nonlinear Priors for Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The techniques of a posteriori image restoration and itera- tive image feature extraction are described and compared. Image feature extraction methods known as Graduated Nonconvexity (GNC), Variable Conductance Diffusion (VCD), Anisotropic Diffusion, and Biased Anisotropic Diffusion (BAD), which extract edges from noisy images, are compared with a restoration/feature extraction method known as Mean Field Annealing (MFA). All are shown to be performing the same basic operation: image relaxation. This equivalence shows the relationship between energy minimization methods and spatial analysis methods and between their respective parameters of temperature and scale. As a result of the equivalence, VCD is demonstrated to minimize a cost function, and that cost is specified explicitly. Furthermore, operations over scale space are shown to be a method of avoiding local minima.