Algorithms for subpixel registration

Tian, Qi; Huhns, Michael N.

doi:10.1016/0734-189x(86)90028-9

Cited by 323 publications

(165 citation statements)

References 5 publications

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“…Therefore, in order to obtain the most accurate strain measurements, subpixel shifts in disk position must be measured. While many subpixel registration algorithms exist [17,18,19,20], several considerations must be taken into account when choosing which to use. Due to the incredibly large size of the datasets obtained, both in file size and number of disk positions to be measured, only the fastest algorithms are considered.…”

Section: Methods For Determining Diffraction Disk Positionsmentioning

confidence: 99%

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

et al. 2017

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Scanning nanobeam electron diffraction strain mapping is a technique by which the positions of diffracted disks sampled at the nanoscale over a crystalline sample can be used to reconstruct a strain map over a large area. However, it is important that the disk positions are measured accurately, as their positions relative to a reference are directly used to calculate strain. In this study, we compare several correlation methods using both simulated and experimental data in order to directly probe susceptibility to measurement error due to non-uniform diffracted disk illumination structure. We found that prefiltering the diffraction patterns with a Sobel filter before performing cross correlation or performing a square-root magnitude weighted phase correlation returned the best results when inner disk structure was present. We have tested these methods both on simulated datasets, and experimental data from unstrained silicon as well as a twin grain boundary in 304 stainless steel.

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Section: Methods For Determining Diffraction Disk Positionsmentioning

confidence: 99%

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

et al. 2017

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“…Using finite differences to the neighboring positions, we expand the quality surface of Equation (3) or (4) into a second order Taylor polynomial around [û,v]. A better estimate of the object position is then given by the coordinates of the extremal point of the resulting quadratic surface, and we can calculate these analytically, see [31].…”

Section: ) Sub-pixel Refinementmentioning

confidence: 99%

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components

Lampert

Peters

2010

J Real-Time Image Proc

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We describe RTblob, a high speed vision system that detects objects in cluttered scenes based on their color and shape at a speed of over 800 frames per second. Because the system is available as open-source software and relies only on off-the-shelf PC hardware components, it can provide the basis for multiple application scenarios. As an illustrative example, we show how RTblob can be used in a robotic table tennis scenario to estimate ball trajectories through 3D space simultaneously from four cameras images at a speed of 200 Hz.

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“…Therefore, when applied to discrete images, the method would fail to detect non-integer subpixel shifts. The only approach, proposed in the literature, for adapting the method for subpixel estimation is the use of interpolation methods [lo] [15].…”

Section: Let Alsomentioning

confidence: 99%

“…Amongst this class of algorithms we can notably mention: correlation interpolation [4] [15], intensity interpolation[l5], phase correlation interpolation [lo] [15] and the geometric methods [a]. It is obvious that the accuracy of these methods depends highly on the quality of the interpolation algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these methods, rely on the differential properties of image frames [B] [7] [15]. There are, however, several difficulties associated with differential methods: they can only be used when the interframe displacements are very small compared to intensity variations, image gradient is only approximated using finite difference methods and finally these methods exhibit high sensitivity to noise.…”

Section: Introductionmentioning

confidence: 99%

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Subpixel image registration by estimating the polyphase decomposition of cross power spectrum

Shekarforoush

Berthod

Zerubia

1996

Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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Algorithms for subpixel registration

Cited by 323 publications

References 5 publications

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components

Subpixel image registration by estimating the polyphase decomposition of cross power spectrum

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