IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical first-difference edge detection with subpixel accuracy
Image and Vision Computing
Uniformly high order accurate essentially non-oscillatory schemes, 111
Journal of Computational Physics
Subpixel Measurements Using a Moment-Based Edge Operator
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated grading of venous beading
Computers and Biomedical Research
Modeling Edges at Subpixel Accuracy Using the Local Energy Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Location Error of Curved Edges in Low-Pass Filtered 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correction for the Dislocation of Curved Surfaces Caused by the PSF in 2D and 3D CT Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subpixel edge location based on orthogonal Fourier-Mellin moments
Image and Vision Computing
Non-linear fourth-order image interpolation for subpixel edge detection and localization
Image and Vision Computing
High-accuracy edge detection with Blurred Edge Model
Image and Vision Computing
Sub-pixel edge detection based on an improved moment
Image and Vision Computing
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Edge Location to Subpixel Values in Digital Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
An edge-guided image interpolation algorithm via directional filtering and data fusion
IEEE Transactions on Image Processing
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The estimation of edge features, such as subpixel position, orientation, curvature and change in intensity at both sides of the edge, from the computation of the gradient vector in each pixel is usually inexact, even in ideal images. In this paper, we present a new edge detector based on an edge and acquisition model derived from the partial area effect, which does not assume continuity in the image values. The main goal of this method consists in achieving a highly accurate extraction of the position, orientation, curvature and contrast of the edges, even in difficult conditions, such as noisy images, blurred edges, low contrast areas or very close contours. For this purpose, we first analyze the influence of perfectly straight or circular edges in the surrounding region, in such a way that, when these conditions are fulfilled, the features can exactly be determined. Afterward, we extend it to more realistic situations considering how adverse conditions can be tackled and presenting an iterative scheme for improving the results. We have tested this method in real as well as in sets of synthetic images with extremely difficult edges, and in both cases a highly accurate characterization has been achieved.