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Motion estimation from noisy image sequences using new frequency weighting functions
(Maltepe Üniversitesi, 2019) El Mehdi, Alaoui İsmaili
Motion estimation is a signal-matching technique. It is a key component of target tracking, medical imaging, video compression, and many other systems. This paper presents a four new estimators for frame-to-frame image motion estimation. The estimators of interest are the ROTH impulse response, the smoothed coherence transform (SCOT), the maximum likelihood (ML) and the Wiener estimators. These are all referred to as Generalized Cross-Correlation (GCC)-estimators. These estimators are based on the cross-correlation of the received images and various weighting functions are used to prefilter the received images before crosscorrelation. As the performances of the GCC-estimators are considerably degraded by the signal-to-noise ratio (SNR) level, this factor has been taken as a prime factor in benchmarking the different GCC-estimators. For robust motion estimation it has been found that the GCC-Wiener is particularly suited to this purpose. The accuracy of the estimators is also discussed.