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dc.contributor.authorEl Mehdi, Alaoui İsmaili
dc.identifier.citationEl Mehdi, İ. A. (2019). Motion estimation from noisy image sequences using new frequency weighting functions. International Conference of Mathematical Sciences (ICMS 2019). s. 149.en_US
dc.description.abstractMotion 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.en_US
dc.publisherMaltepe Üniversitesien_US
dc.rightsCC0 1.0 Universal*
dc.subjectMotion estimationen_US
dc.subjectWhitening functionen_US
dc.subjectNoisy image sequencesen_US
dc.titleMotion estimation from noisy image sequences using new frequency weighting functionsen_US
dc.relation.journalInternational Conference of Mathematical Sciences (ICMS 2019)en_US
dc.contributor.departmentMaltepe Üniversitesi, İnsan ve Toplum Bilimleri Fakültesien_US
dc.relation.publicationcategoryUluslararası Konferans Öğesi - Başka Kurum Yazarıen_US
dc.contributor.institutionauthorEl Mehdi, Alaoui İsmaili

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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal