A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Localization Performance Measure and Optimal Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimal linear operator for step edge detection
CVGIP: Graphical Models and Image Processing
On the Edge Location Error for Local Maximum and Zero-Crossing Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Multi-edge detection by isotropical 2-D ISEF cascade
Pattern Recognition
Multiscale extension of the gravitational approach to edge detection
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Quantitative error measures for edge detection
Pattern Recognition
Multiscale edge detection based on Gaussian smoothing and edge tracking
Knowledge-Based Systems
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Canny (IEEE Trans. Pattern Anal. Image Proc. 8(6):679-698, 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny's work suffers from two problems. First, his derivation of localization criterion is incorrect. Here we provide a more accurate localization criterion and derive the optimal detector from it. Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector. The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image. If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph. Models Image Proc. 54:112---133, 1992).In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian. This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image. We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions.