2014
DOI: 10.1364/ol.39.006715
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Improving the accuracy performance of phase-shifting profilometry for the measurement of objects in motion

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Cited by 38 publications
(7 citation statements)
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“…In this work, we propose a scheme to combine the height maps retrieved from the multiple images captured, such that the influence from noise can be alleviated. In this paper, we assume rigid movements and that knowledge of the motion parameters is available, e.g., acquired using mark-based techniques [16].…”
Section: Motion Compensation For Moving Object Measurementsmentioning
confidence: 99%
See 1 more Smart Citation
“…In this work, we propose a scheme to combine the height maps retrieved from the multiple images captured, such that the influence from noise can be alleviated. In this paper, we assume rigid movements and that knowledge of the motion parameters is available, e.g., acquired using mark-based techniques [16].…”
Section: Motion Compensation For Moving Object Measurementsmentioning
confidence: 99%
“…(14) Using (14), we can estimate the SNR for the images as {Γ }. Similarly to [16], we then set the combining weights in (11) as…”
Section: Motion Compensation For Moving Object Measurementsmentioning
confidence: 99%
“…Additionally, other desirable properties for phase-shifting techniques are a reduced number of necessary fringe patterns, maximum contrast, and the capacity to handle spatiotemporal visibility (i.e., the background and modulation light are functions of both p and k) [37,59,[60][61][62]. For this, a generalized phase-shifting algorithm was proposed [21].…”
Section: Generalized Phase-shifting Algorithm To Inhomogeneous Phamentioning
confidence: 99%
“…Cong et al proposed a Fourier-assisted PSP approach, which corrected the phase shift error by differentiating the phase maps of two successive fringe images [12]. In addition, Lu et al suggested refining the unknown phase shifts using the least-squares method with constraints of the background intensity and the modulation amplitude [13]. Feng et al divided motion errors into three categories: motion ripples, motion-induced phase unwrapping errors, and motion outliers [14].…”
Section: Introductionmentioning
confidence: 99%