This paper addresses the problem to extract moving object from the moving background in the mixed domain (MixeD), which makes it possible to carry the filtering in one dimension. Since the velocities of moving object and background are necessary for moving object extraction, we first estimate the velocities based on the appropriate spatial frequency point selection method in the MixeD. Then an optimal filter used for 1-D signal filtering is designed. By filtering 1-D signals over all spatial frequencies, signals with certain velocity vector are extracted, while the extracted image is obtained by applying the 2-D IDFT to the filtered signals. The simulation results show that the method can extract moving object based both on the correctly estimated velocity and the proposed optimal 1-D filter.

To estimate moving object velocity in an image sequence is useful for a variety of applications, such as velocity measurement, computer vision and monitoring systems. An effective way is to approach it in the transform/spatiotemporal mixed domain (MixeD), which transforms the 3-D signal processing problem into 1-D complex signal processing. But it remains a problem how to select several spatial frequency points in the MixeD which may influence the accuracy of velocity estimation and object detection. In this paper, a spatial frequency selection method has been proposed, which can choose the appropriate spatial frequency points out of a number of points in MixeD automatically. So the velocity estimation problem can be addressed by solving the coupled equations established over two selected appropriate points in 2-D spatial frequency domain other than searching for the spectral energy plane over a number of points selected by experience. In this method, evaluation functions corresponding to image sequence with one moving object and two moving objects are established firstly, and the selection is then achieved by using the established evaluation functions together with a threshold. The simulation results show that the proposed method is effective on the appropriate spatial frequency selection.

Recently, the spatio-temporal filter using linear analog Cellular Neural Network (CNN), called CNN filter array, has been proposed for the purpose of dynamic image processing. In this paper, we propose a design method of descrete-time cellular neural network filter which selectively extracts the particular moving object from other moving objects and noise. The CNN filter array forms a spatio-temporal filter by arranging cells with a same function. Each of these cells is a simple linear analog temporal filter whose input is the weighted sum of its neighborhood inputs and outputs and each cell corresponds to each pixel. The CNN filter is formed by new model of discrete time CNN, and the filter parameters are determined by applying backpropagation algorithm in place of the analytic method. Since the number of connections between neurons in the CNN-type filter is relatively few, the required computation in the learning phase is reasonable amount. Further, the output S/N ratio is improved by introducing nonlinear element. That is, if the ratio of output to imput is smaller than a certain value, the output signal is treated as a noise signal and ought to be rejected. Through some examples, it is shown that the target object is enhanced in the noisy environment.

In the design of 3-D filter detecting Linear Trajectory Signal (LTS), there may be paid little attention to the noise rejective characteristics. In this paper, we treat the noise rejection ability of the filter detecting LTS having margins both in its velocity and direction.