A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pulse and staircase edge models
Computer Vision, Graphics, and Image Processing
Geometric Precision in Noise-Free Digital Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subpixel Measurements Using a Moment-Based Edge Operator
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
On the Precision in Estimating the Location of Edges and Corners
Journal of Mathematical Imaging and Vision
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing Edge Models via Local Principal Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Journal of Mathematical Imaging and Vision
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Barankin-type lower bound on multiple change-point estimation
IEEE Transactions on Signal Processing
Fundamental limits in 3d landmark localization
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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Edge localization occurs when an edge detector determines the location of an edge in an image. The authors use statistical parameter estimation techniques to derive bounds on achievable accuracy in edge localization. These bounds, known as the Cramer-Rao bounds, reveal the effect on localization of factors such as signal-to-noise ratio (SNR), extent of edge observed, scale of smoothing filter, and a priori uncertainty about edge intensity. By using continuous values for both image coordinates and intensity, the authors focus on the effect of these factors prior to sampling and quantization. They also analyze the Canny algorithm and show that for high SNR, its mean squared error is only a factor of two higher than the lower limit established by the Cramer-Rao bound. Although this is very good, the authors show that for high SNR, the maximum-likelihood estimator, which is also derived, virtually achieves the lower bound.