In the usual optical flow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical flow detection technique. Since this method requires the knowledge about the covariance matrix of the noise, we also discuss a method for its estimation. Our experiments show that the proposed method can compute optical flow more accurately than the conventional method.

This paper discusses the fixed-point smoothing and filtering problems given lumped covariance function of a scalar signal process observed with additive white Gaussian noise. The recursive Wiener smoother and filter are derived by applying an invariant imbedding method to the Volterra-type integral equation of the second kind in linear least-squares estimation problems. The resultant estimators in Theorem 2 require the information of the crossvariance function of the state variable with the observed value, the system matrix, the observation vector, the variance of the observation noise and the observed value. Here, it is assumed that the signal process is generated by the state-space model. The spectral factorization problem is also considered in Sects. 1 and 2.

A new adaptive AR spectral estimation method is proposed. While conventional least-squares methods use a single windowing function to analyze the linear prediction error, the proposed method uses a different window for each frequency band of the linear prediction error to define a cost function to be meinemized. With this approach, since time and frequency resolutions can be traded off throughout the frequency spectrum, an improvement on the precision of the estimates is achieved. In this paper, a wavelet-like time-frequency resolution grid is used so that low-frequency components of the linear prediction error are analyzed through long windows and high-frequency components are analyzed through short ones. To solve the optimization problem for the new cost function, special properties of the correlation matrix are used to derive an RLS algorithm on the order of M2, where M is the number of parameters of the AR model. Computer simulations comparing the performance of conventional RLS and the proposed methods are shown. In particular, it can be observed that the wavelet-based spectral estimation method gives fine frequency resolution at low frequencies and sharp time resolution at high frequencies, while with conventional methods it is possible to obtain only one of these characteristics.

This letter presents new estimation algorithm of ARMAX systems which do not always satisfy the strictly positive real (SPR) condition. We show how estimated parameters can converge to their true values based on the overparameterized system. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

Numerical simulation of the hydrodynamic semiconductor device equations requires powerful numerical schemes. A Space-time Galerkin/Least-Squares finite element formulation, that has been successfully applied to problems of fluid dynamic, is proposed for the solution of the hydrodynamic device equations. Similarity between the equations of fluid dynamic and semiconductor devices is discussed. The robustness and accuracy of the numerical scheme are demonstrated with the example of a single electron carrier submicron silicon MESFET device.

Radar imaging technique is one of the most powerful tool for underground detection. However, performance of conventional methods is not sufficiently high when the observational direction or the aperture size is restricted. In the present paper, an image reconstruction method based on a model fitting with nonlinear least-squares has been developed, which is applicable to arbitrarily arranged arrays. Reconstruction is executed on the assumption that targets consist of discrete point scatterers embedded in a homogeneous medium. Model fitting is iterated as the number of point target in the assumed model is increased, until the residual in fitting becomes unchanged or small enough. A penalty function is used in nonlinear least-squares to make the algorithm stable. Fundamental characteristics of the method revealed with computer simulation are described. This method focuses a much sharper image than that obtained by the conventional aperture synthesis technique.

This paper presents a technique by which any linear CCD camera, be it one with lens distortions, or even one with misaligned lens and CCD, may be calibrated to obtain optimum performance characteristics. The camera-image formation model is described as a polynomial expression, which provides the line-of-sight flat-beam, including the target light-spot. The coefficients of the expression, which are referred to as camera parameters, can be estimated using the linear least-squares technique, in order to minimize the discrepancy between the reference points and the model-driven flat-beam. This technique requires, however, that a rough estimate of camera orientation, as well as a number of reference points, are provided. Experiments employing both computer simulations and actual CCD equipment certified that the model proposed can accurately describe the system, and that the parameter estimation is robust against noise.