Translational displacement estimation with subpixel accuracy of DCT-sign phase correlation

2015 ◽  
Author(s):  
Izumi Ito
Keyword(s):  
Phase Correlation ◽  
Displacement Estimation ◽  
Subpixel Accuracy ◽  
Translational Displacement

Phase Correlation Based Image Alignment with Subpixel Accuracy

2013 ◽  
pp. 171-182 ◽  
Author(s):  
Alfonso Alba ◽  
Ruth M. Aguilar-Ponce ◽  
Javier Flavio Vigueras-Gómez ◽  
Edgar Arce-Santana
Keyword(s):  
Phase Correlation ◽  
Image Alignment ◽  
Subpixel Accuracy

scholarly journals Phase correlation functions: FFT vs. FHT

ACTA IMEKO ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 87 ◽  
Author(s):  
Giuseppe Schirripa Spagnolo ◽  
Lorenzo Cozzella ◽  
Fabio Leccese
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Correlation Function ◽  
Pattern Matching ◽  
Phase Correlation ◽  
Critical Element ◽  
Hartley Transform ◽  
Subpixel Accuracy ◽  
2D Fft ◽  
Good Substitute

<p><span lang="EN-GB">The ability to process an image is a crucial skill in many measurement activities. In image processing or pattern recognition, Fast Fourier Transform (FFT) is widely used. In particular, the Phase Only Correlation (POC) method demonstrates high robustness and subpixel accuracy in pattern matching. However, there is a disadvantage in the required memory machine because of the calculation of 2D-FFT. In applications in which the use of memory is a critical element, Fast Hartley Transform (FHT) seems to be a good substitute. In this context, the use of Hartley’s transform can be of interest for apps implemented on portable systems e.g. smartphones. In this article, we present a comparison of the implementations of the phase correlation function using FFT and FHT. Particular attention is given to the analytical steps necessary to implement the POC by means of the Hartley transform.</span></p>

Walking displacement estimation using waist moving model

2013 ◽  
Vol 26 (8) ◽  
pp. 711-715
Author(s):  
Kaitian Ren ◽  
Yu Liu ◽  
Shaochu Wang
Keyword(s):  
Displacement Estimation

scholarly journals Displacement Estimation Based on Optical and Inertial Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
External Disturbance ◽  
Linear Displacement ◽  
Measuring System ◽  
Displacement Estimation ◽  
Mems Accelerometer ◽  
Pedestrian Navigation ◽  
Complementary Filter ◽  
Optical Flow Sensor

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

Displacement estimation from measured acceleration for fixed offshore structures

2021 ◽  
Vol 113 ◽  
pp. 102741
Author(s):  
Fushun Liu ◽  
Shujian Gao ◽  
Shuang Chang
Keyword(s):  
Offshore Structures ◽  
Displacement Estimation
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