A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis Using Multigrid Relaxation Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Statistical Inference for Spatial Processes
Statistical Inference for Spatial Processes
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
Coping with Discontinuities in Computer Vision: Their Detection, Classification, and Measurement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Cost Minimization Approach to Edge Detection Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Algorithm for Discontinuity-Preserving Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-preserving and peak-preserving smoothing
Technometrics
Bayesian Unsupervised Learning for Source Separation with Mixture of Gaussians Prior
Journal of VLSI Signal Processing Systems
| Hi-index | 0.14 |
Bayesian methods for recovering a 2-D surface are discussed. It is assumed that there is a textural image that can be modeled by a Markov random field and that the original surface is composed of different surfaces, each of which is associated with one textural state. Both parametric and nonparametric methods are used to enforce smoothness of these surfaces. Iterative procedures are examined for simultaneous restoration of the textural image and estimation of underlying parameters. From the estimated textural image and the estimated parameters, an estimate for the original surface is obtained. Two illustrative examples are presented.