Dynamic threshold determination by local and global edge evaluation
Graphical Models and Image Processing
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Blind restoration of atmospherically degraded images by automatic best step-edge detection
Pattern Recognition Letters
On candidates selection for hysteresis thresholds in edge detection
Pattern Recognition
Hi-index | 0.00 |
The basic and widely used edge detection operation in an image usually requires a prior step of setting the edge detector parameters (thresholds, blurring extent etc.). Finding the best detector parameters automatically in real-world images is a difficult challenge because no absolute ground truth exists. However, the advantage of automatic processing over manual operations done by humans motivates the development of automatic detector parameter selection. In this work, we propose an automatic detector parameter selection which considers both, statistical correspondence of detection results produced from different detector parameters, and spatial correspondence between detected edge points, represented as saliency values. The method improves a recently developed technique that employs only statistical correspondence of detection results and depends on the initial parameter range by incorporating saliency values in the statistical analysis. Automatic edge detection results show considerable improvement of the purely statistical method when a wrong initial parameter range is selected.