Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Design of Filters for Gradient-Based Motion Estimation
Journal of Mathematical Imaging and Vision
A new edge detector based on Fresnel diffraction
Pattern Recognition Letters
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Omnidirectional edge detection
Computer Vision and Image Understanding
Facet detection and visualizing local structure in graphs
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
A new methodology for evaluation of edge detectors
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Boolean derivatives with application to edge detection for imaging systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Quantitative error measures for edge detection
Pattern Recognition
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This paper presents an evaluation of edge detector performance. We use the task of structure from motion (SFM) as a “black box” through which to evaluate the performance of edge detection algorithms. Edge detector goodness is measured by how accurately the SFM could recover the known structure and motion from the edge detection of the image sequences. We use a variety of real image sequences with ground truth to evaluate eight different edge detectors from the literature. Our results suggest that ratings of edge detector performance based on pixel-level metrics and on the SFM are well correlated and that detectors such as the Canny detector and Heitger detector offer the best performance