A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric tests for edge detection in noise
Pattern Recognition
Statistical theory of edge detection
Computer Vision, Graphics, and Image Processing
Edge evaluation using necessary components
CVGIP: Graphical Models and Image Processing
Analysis of Variance in Statistical Image Processing
Analysis of Variance in Statistical Image Processing
Detection of linear and circular shapes in image analysis
Computational Statistics & Data Analysis
Editorial: Nonparametric and Robust Methods
Computational Statistics & Data Analysis
On the robust detection of edges in time series filtering
Computational Statistics & Data Analysis
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Investigating particle swarm optimisation topologies for edge detection in noisy images
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Original Articles: Nonparametric edge detection in speckled imagery
Mathematics and Computers in Simulation
Automatic construction of invariant features using genetic programming for edge detection
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Information Sciences: an International Journal
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We describe a new edge detector based on the robust rank-order (RRO) test which is a useful alternative to Wilcoxon test, using rxr window for detecting edges of all possible orientations in noisy images. Our method is based on testing whether a rxr window is spatially partitioned into two subregions having significant differences in local gray-level value between adjacent pixel neighborhoods of a given pixel, using an edge-height model to extract edges of some sufficient height from images corrupted with noises. Some experiments of statistical edge detectors based on the Wilcoxon test and T-test, and the well-known Canny edge detector with our RRO detector are carried out on synthetic and real images corrupted by both Gaussian and impulse noise. The results show that the performance of the proposed edge detector appears to be the most robust to variations in noise, performing well in all noise distributions tested.