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Proceedings Paper

Uncertainty quantification of phase-based motion estimation on noisy sequence of images
Author(s): Aral Sarrafi; Zhu Mao
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Paper Abstract

Optical measurement and motion estimation based on the acquired sequence of images is one of the most recent sensing techniques developed in the last decade or so. As a modern non-contact sensing technique, motion estimation and optical measurements provide a full-field awareness without any mass loading or change of stiffness in structures, which is unavoidable using other conventional transducers (e.g. accelerometers, strain gauges, and LVDTs). Among several motion estimation techniques prevalent in computer vision, phase-based motion estimation is one of the most reliable and accurate methods. However, contamination of the sequence of images with numerous sources of noise is inevitable, and the performance of the phase-based motion estimation could be affected due to the lighting changes, image acquisition noise, and the camera’s intrinsic sensor noise. Within this context, the uncertainty quantification (UQ) of the phase-based motion estimation (PME) has been investigated in this paper. Based on a normality assumption, a framework has been provided in order to characterize the propagation of the uncertainty from the acquired images to the estimated motion. The established analytical solution is validated via Monte-Carlo simulations using a set of simulation data. The UQ model in the paper is able to predict the order statistics of the noise influence, in which the uncertainty bounds of the estimated motion are given, after processing the contaminated sequence of images.

Paper Details

Date Published: 5 April 2017
PDF: 8 pages
Proc. SPIE 10170, Health Monitoring of Structural and Biological Systems 2017, 101702M (5 April 2017); doi: 10.1117/12.2260403
Show Author Affiliations
Aral Sarrafi, Univ. of Massachusetts Lowell (United States)
Zhu Mao, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 10170:
Health Monitoring of Structural and Biological Systems 2017
Tribikram Kundu, Editor(s)

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