A Statistical Model for Contours in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient, recursively implemented differential operator, with application to edge detection
Pattern Recognition Letters
A new edge detector based on Fresnel diffraction
Pattern Recognition Letters
Laplacian Operator-Based Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Omnidirectional edge detection
Computer Vision and Image Understanding
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
A new effective and powerful image segmentation method
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Image feature detection from phase congruency based on two-dimensional Hilbert transform
Pattern Recognition Letters
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
AddCanny: edge detector for video processing
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels
Pattern Recognition
Edge Detection Filter based on Mumford-Shah Green Function
SIAM Journal on Imaging Sciences
Quantitative error measures for edge detection
Pattern Recognition
Two-dimensional multi-pixel anisotropic Gaussian filter for edge-line segment (ELS) detection
Image and Vision Computing
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In this paper, we revisit the analytical expressions of the three Canny's (1983) criteria for edge detection quality: good detection, good localization, and low multiplicity of false detections. Our work differs from Canny's work on two essential points. Here, the criteria are given for discrete sampled signals, i.e., for the real, implemented filters. Instead of a single-step edge as input signal, we use pulses of various width. The proximity of other edges affects the quality of the detection process. This is taken into account in the new expressions of these criteria. We derive optimal filters for each of the criteria and for any combination of them. In particular, we define an original filter which maximizes detection and localization and a simple approximation of the optimal filter for the simultaneous maximization of the three criteria. The upper bounds of the criteria are computed which allow users to measure the absolute and relative performance of any filter (exponential, Deriche (1987), and first derivative of Gaussian filters are evaluated). Our criteria can also be used to compute the optimal value of the scale parameter of a given filter when the resolution of the detection is fixed.