Abstract
Motion estimation techniques are widely used in today's video processing systems. The frequently used techniques are frequency-domain motion estimation methods, most notably phase correlation (PC). If the image frames are corrupted by Gaussian noises, then cross-correlation and related techniques do not work well. In this paper, however, we have studied this topic from a viewpoint different from the above. Our scheme is based on the bispectrum method for sub-pixel motion estimation of noisy image sequences. Experimental results show that our proposed method performs significantly better than PC technique.
Highlights
Image frames are generated by scanning a scene several times a second where each frame, generally, consists of two regions
To prove the feasibility of the proposed method, we compared it to a phase correlation (PC) technique implemented in a similar manner as our approach
All image sequences are degraded with additive zero-mean Gaussian noise to a signal-to-noise ratio (SNR) of 10 dB
Summary
Image frames are generated by scanning a scene several times a second where each frame, generally, consists of two regions. If the image frames are corrupted by Gaussian noises, cross-correlation and related techniques do not work well. In this circumstance, higher-order spectra (HOS) in general and the bispectrum in particular have recently been widely used as an important tool for signal processing. The classical methods based on the power spectrum are being effectively superseded by the bispectral ones due to some definite disadvantages of the former These include the inability to identify systems fed by non-Gaussian noise (NGN) inputs and nonminimum phase (NMP) systems, and by identification of system nonlinearity. In these cases, the autocorrelation-based methods offer no answer. The identifiability of NMP systems has received the maximum attention from researchers
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