Motion estimation based on time-sequentially sampled imagery

Velocity selective filters recursively implemented in the spatiotemporal domain

Energy-based methods for motion estimation in image sequences process the input data either in the spatiotemporal or in the frequency domain. In both cases, the algorithms already described in the literature often require a huge number of elementary operations. In this paper, we describe a class of velocity selective filters which yield an accurate detection of the edges moving in the sequence. We first present a filtering scheme based on a convolution operation computed on a finite size neighborhood and describe its properties in the spatiotemporal and frequency domains. Then, we show that filters with similar properties can be implemented recursively, i.e., as convolutions computed on infinite-size neighborhoods. As an example, we finally show the filters' responses in the case of two superimposed translational motions.

Two-dimensional matched filtering for motion estimation

In this work, we describe a frequency domain technique for the estimation of multiple superimposed motions in an image sequence. The least-squares optimum approach involves the computation of the three-dimensional (3-D) Fourier transform of the sequence, followed by the detection of one or more planes in this domain with high energy concentration. We present a more efficient algorithm, based on the properties of the Radon transform and the two-dimensional (2-D) fast Fourier transform, which can sacrifice little performance for significant computational savings. We accomplish the motion detection and estimation by designing appropriate matched filters. The performance is demonstrated on two image sequences.

Multiresolution dynamic image representation with uniform and foveal spiral scan data

This work addresses the problem of representing a dynamic image via its temporal spiral scan data. Two types of spiral scan data are considered: uniform density and foveal. Spatial sampling strategies for these two spiral scans are examined. A signal model is developed to interpret the temporal readouts of repeated spiral scans via two separate time variables, the slow time and fast time. This mathematical model is used to construct a method for forming the time progression of the target image. A method for increasing the repetition rate of the spiral data collection via utilizing both forward and backward spiral scans is presented. Results are provided.