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.

In this paper, detection and estimation of rotational motion in the 3-D Gabor domain are investigated. By extending frequency domain methods to the joint spatiotemporal/spatio-temporal-frequency (st/stf) analysis framework, we propose a technique to estimate multiple localized rotational motions in image sequences. Based on a rotational motion model derived in the 3-D Gabor domain, an algorithm for estimating localized uniform rotational motions is constructed and discussed.

This paper presents two advancements in motion estimation based on the 3-D Gabor representation. First, we introduce a new searching strategy and solve the estimation bias problem encountered in the current approach to piecewise translational motion estimation; second, in order to achieve better spatiotemporal resolution, we constructed a new algorithm that can provide dense motion field estimation.