A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
A Discrete Expression of Canny's Criteria for Step Edge Detector Performances Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of edge detectors: a methodology and initial study
Computer Vision and Image Understanding
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detector evaluation using empirical ROC curves
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Operator-Based Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of edge detection algorithms using a structure frommotion task
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal edge detection in two-dimensional images
IEEE Transactions on Image Processing
Directional filtering in edge detection
IEEE Transactions on Image Processing
On optimal linear filtering for edge detection
IEEE Transactions on Image Processing
A simple and efficient edge detection method
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Edge detection in the feature space
Image and Vision Computing
Generating fuzzy edge images from gradient magnitudes
Computer Vision and Image Understanding
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In this paper we propose a new method for extending 1-D step edge detection filters to two dimensions via complex-valued filtering. Complex-valued filtering allows us to obtain edge magnitude and direction simultaneously. Our method can be viewed either as an extension of n-directional complex filtering of Paplinski to infinite directions or as a variant of Canny's gradient-based approach. In the second view, the real part of our filter computes the gradient in the x direction and the imaginary part computes the gradient in the y direction. Paplinski claimed that n-directional filtering is an improvement over the gradient-based method, which computes gradient only in two directions. We show that our omnidirectional and Canny's gradient-based extensions of the 1-D DoG coincide. In contrast to Paplinski's claim, this coincidence shows that both approaches suffer from being confined to the subspace of two 2-D filters, even though n-directional filtering hides these filters in a single complex-valued filter. Aside from these theoretical results, the omnidirectional method has practical advantages over both n-directional and gradient-based approaches. Our experiments on synthetic and real-world images show the superiority of omnidirectional and gradient-based methods over n-directional approach. In comparison with the gradient-based method, the advantage of omnidirectional method lies mostly in freeing the user from specifying the smoothing window and its parameter. Since the omnidirectional and Canny's gradient-based extensions of the 1-D DoG coincide, we have based our experiments on extending the 1-D Demigny filter. This filter has been proposed by Demigny as the optimal edge detection filter in sampled images.