Title :
Motion Estimation in the 3-D Gabor Domain
Author :
Feng, Mu ; Reed, Todd R.
Author_Institution :
Hawaii Univ., Honolulu
Abstract :
Motion estimation methods can be broadly classified as being spatiotemporal or frequency domain in nature. The Gabor representation is an analysis framework providing localized frequency information. When applied to image sequences, the 3-D Gabor representation displays spatiotemporal/spatiotemporal-frequency (st/stf) information, enabling the application of robust frequency domain methods with adjustable spatiotemporal resolution. In this work, the 3-D Gabor representation is applied to motion analysis. We demonstrate that piecewise uniform translational motion can be estimated by using a uniform translation motion model in the st/stf domain. The resulting motion estimation method exhibits both good spatiotemporal resolution and substantial noise resistance compared to existing spatiotemporal methods. To form the basis of this model, we derive the signature of the translational motion in the 3-D Gabor domain. Finally, to obtain higher spatiotemporal resolution for more complex motions, a dense motion field estimation method is developed to find a motion estimate for every pixel in the sequence.
Keywords :
frequency-domain analysis; image representation; image resolution; image sequences; motion estimation; 3D Gabor domain; 3D Gabor representation; frequency domain methods; image sequences; motion estimation; piecewise uniform translational motion; spatiotemporal resolution; spatiotemporal-frequency information; Frequency domain analysis; Image analysis; Image motion analysis; Image processing; Image sequences; Motion analysis; Motion estimation; Spatiotemporal phenomena; Video compression; Working environment noise; 3-D Gabor representation; Dense motion field estimation; motion estimation; spatiotemporal/spatiotemproal-frequency analysis; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Motion; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2007.901812