Algorithms for subpixel registration
Computer Vision, Graphics, and Image Processing
A geometric approach to subpixel registration accuracy
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Precision in Noise-Free Digital Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of Fiducials for Accurate Registration Using Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Topology of Locales and Its Effects on Position Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Sampling of Printed Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multigrid Convergence of Calculated Features in Image Analysis
Journal of Mathematical Imaging and Vision
Machine vision system for inspecting electric plates
Computers in Industry
Calibration-Free Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
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The precision by which a region is located or measured on the image plane is limited by the sampling density. In this paper, the worst-case precision errors are determined for calculating the average image location of an edge, line, and straight-edged region. For each case, it is shown how the worst-case error can be minimized as a function of the geometric parameters. These results can be used to determine the worst case error by which the location of a known shape is measured. Another application is to design shapes for use in registration, such as fiducial marks used in electronic assembly. The main conclusion of this paper is that, to achieve better precision, measurement of a straight-edged region should be made at an angle askew to the sampling axis (not 0, 45, or 90 degrees) and this should be at a certain length that is a function of this skew angle.