Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to mathematical morphology
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Dimensional Regularization with Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior of Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Edge and Line Feature Extraction Based on Covariance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Gray-Scale Extraction of Features for Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new edge detector based on Fresnel diffraction
Pattern Recognition Letters
A statistical framework based on a family of full range autoregressive models for edge extraction
Pattern Recognition Letters
| Hi-index | 0.14 |
It is shown that in a very simple form residual analysis achieves results that are at least as good as if not better than those obtained by other techniques. There are many ways for extensions of the method. For example, moving average filters of regularization can be used to obtain the residual images. Also, the strength of the correlation, measured by D/sub rr/(O), can be used to eliminate noise, weak edges, etc. A more ambitious extension is by considering smoothing filters that leave invariant the function representing the reflectance from smooth surfaces.