A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation labelling algorithms-a review
Image and Vision Computing
Radial Projection: An Efficient Update Rule for Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-Labeling Using Dictionary-Based Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion. II
SIAM Journal on Numerical Analysis
Nonlinear Image Filtering with Edge and Corner Enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Minimization of MRF Energy with Relaxation Labeling
Journal of Mathematical Imaging and Vision
Geometric shock-capturing eno schemes for subpixel interpolation, computation and curve evolution
Graphical Models and Image Processing
A neural model of contour integration in the primary visual cortex
Neural Computation
A Local Visual Operator Which Recognizes Edges and Lines
Journal of the ACM (JACM)
Binary digital image processing: a discrete approach
Binary digital image processing: a discrete approach
Lattice Boltzmann Models for Anisotropic Diffusion of Images
Journal of Mathematical Imaging and Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Local Parallel Computation of Stochastic Completion Fields
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
| Hi-index | 0.00 |
The paper describes a robust edge and contour extraction technique under two types of degradation: random noise and aliasing. The technique employs unambiguous probabilistic relaxation to distinguish features from noise and refine their spatial locations at subpixel accuracy. The most important component in the probabilistic relaxation is a compatibility function. The paper suggests a function with which the optimal orientation of edges can be derived analytically, thus allowing an efficient implementation of the relaxation process. A contour extraction algorithm is designed by combining the relaxation process and a perceptual organization technique. Results on both synthetic and natural images are given and show effectiveness of our approach against noise and aliasing.