Graph cut minimization formulates the segmentation problem as the liner combination of data and smoothness terms. The smoothness term is included in the energy formulation through a regularization parameter. We propose that the trade-off between the data and the smoothness terms should not be balanced by the same regularization parameter for the whole image. In order to validate the proposed id...

the forecasting of hydrological variables, such as streamflow, plays an important role in water resource planning and management. recently, the development of stochastic models is regarded as a major step for this purpose. streamflow forecasting using the arima model can be conducted when unknown parameters are estimated correctly because parameter estimation is one of the crucial steps in mode...

The computation of 2-D optical flow by means of regularized pel-recursive algorithms raises a host of issues, which include the treatment of outliers, motion discontinuities and occlusion among other problems. We propose a new approach which allows us to deal with these issues within a common framework. Our approach is based on the use of a technique called Generalized Cross-Validation to estim...

Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the re...

In automatic control and its related applications, many problems can be formulated as the regression estimation problem. In this paper, we construct a nonlinear regression model by using kernels as basis functions in a dictionary and applying the L1 norm as the regularizer. The regression function obtained from this model possesses the sparseness property where only a subset of points are used ...

Though popular in the geophysical modeling community, specification of spatially distributed parameters at a scale commensurate with prevailing geological heterogeneity has not been possible in common groundwater modeling practice. The principal reasons for this are (1) the high computational burden of obtaining derivatives necessary for parameter estimation, (2) the memory required to store th...

Regularization is a required component of geophysical estimation problems that operate with insufficient data. The goal of regularization is to impose additional constraints on the estimated model. I introduce shaping regularization, a general method for imposing constraints by explicit mapping of the estimated model to the space of admissible models. Shaping regularization is integrated in a c...

Personal audio systems generate a local sound field for a listener while attenuating the sound energy at pre-defined quiet zones. In practice, system performance is sensitive to errors in the acoustic transfer functions between the sources and the zones. Regularization is commonly used to improve robustness, however, selecting a regularization parameter is not always straightforward. In this pa...

The effectiveness of regularization to improve SNR in parallel imaging techniques has been reported in previous works [1-2], but how to optimize the regularization parameter remains a problem. The regularization parameter controls the degree of regularization and thereby determines the compromise between SNR and artifacts. Over-regularization causes high level of artifact, while under-regulariz...

The method of moments proposed by Carrasco and Florens (2000) permits to fully exploit the information contained in the characteristic function and yields an estimator which is asymptotically as effi cient as the maximum likelihood estimator. However, this estimation procedure depends on a regularization or tuning parameter α that needs to be selected. The aim of the present paper is to provide...