The need for motion estimation (ME) arises quite often in many areas such as computer vision, target tracking, medical imaging, robotic vision. A five new estimators for frame-to-frame image ME are described in this paper. The new ME estimators exploit the higher-order statistics (HOS) characteristics of the received images, and various frequency weighting functions are used to prefilter the received images before calculating the generalized cross-cumulant function and, therefore, suppress the Gaussian noise effect. The estimators of interest are the HOS-ROTH impulse response, the HOS-phase transform, the HOS-smoothed coherence transform, the HOS-maximum likelihood and the HOS-Wiener estimators. Since the performances of the HOS-based estimators are considerably degraded by the signal-to-noise ratio level, this factor has been taken as a prime factor in benchmarking the different estimators. For robust ME it has been found that the HOS-Wiener estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed.
"Signal, Image and Video Processing" – Springer Journals
Published: Apr 26, 2017
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