A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Dimensional Regularization with Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimal Scale for Edge Detection
An Optimal Scale for Edge Detection
λτ-Space Representation of Images and Generalized Edge Detector
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface smoothing for enhancement of 3d data using curvature-based adaptive regularization
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Edge Drawing: A combined real-time edge and segment detector
Journal of Visual Communication and Image Representation
Adaptive regularization-based space-time super-resolution reconstruction
Image Communication
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An edge detection and surface reconstruction algorithm in which the smoothness is controlled spatially over the image space is presented. The values of parameters in the model are adaptively determined by an iterative refinement process; hence, the image-dependent parameters such as the optimum value of the regularization parameter or the filter size are eliminated. The algorithm starts with an oversmoothed regularized solution and iteratively refines the surface around discontinuities by using the structure exhibited in the error signal. The spatial control of smoothness is shown to resolve the conflict between detection and localization criteria of edge detection by smoothing the noise in continuous regions while preserving discontinuities. The performance of the algorithm is quantitatively and qualitatively evaluated on real and synthetic images, and it is compared with those of Marr-Hildreth and Canny edge detectors.