Image Motion Estimation From Motion Smear-A New Computational Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of motion parameters from blurred images
Pattern Recognition Letters
Stochastic Global Optimization: Problem Classes and Solution Techniques
Journal of Global Optimization
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
A biologically inspired method for estimating 2D high-speed translational motion
Pattern Recognition Letters
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Previous vision-based methods usually measure vibration from large sequence of unblurred images recorded by high-speed video or stroboscopic photography. In this paper, we propose a novel method for sinusoidal vibration measurement based on motion blurred images. We represent the motion blur information in images by the relationship between the geometric moments of the motion blurred images and the motion, and estimate the vibration parameters from this motion blur cue. We need only one motion blurred image and an unblurred image or two successive frames of blurred images to calculate the parameters of low-frequency vibration as well as the amplitude and direction of high-frequency vibration, while unblurred-image-based techniques rely on much more images to obtain the same results and existing motion-blurred-image-based approaches only estimate the amplitude of high-frequency vibration. Experimental results with both simulated and real vibrations of low and high frequencies are employed to demonstrate the effectiveness of the proposed scheme.