This article introduces a new inferential test for acyclic structural equation models (SEM) without latent variables or correlated errors. The test is based on the independence relations predicted by the directed acyclic graph of the SEMs, as given by the concept of d-separation. A wide range of distributional assumptions and structural functions can be accommodated. No iterative fitting proced...

In this research, we introduce a reasonable noise model for range data which is obtained by a laser radar range jnder, and derive two simple approximate solutions of the optimal local planejtting the range data under the noise model. Then we compare our methods with the general least-squares based methods, such as Z-function fitting, the eigenvalue method, and the maximum likelihood estimation ...

We present a new application of computer vision: continuous measurement of the geometrical visibility range on inter-urban roads, solely based on a monocular image acquisition system. To tackle this problem, we propose first a road segmentation scheme based on a Parzenwindowing of a color feature space with an original update that allows us to cope with heterogeneously paved-roads, shadows and ...

This paper presents a robust vision algorithm for tracking the boundary of an object with an arbitrary shape by using monocular image sequences. This method consists of a curve registration based optimization technique and a deformable contour model (”snakes”) for the global and the local motion estimations, respectively. By combining techniques, we overcome, among other problems, inaccurate es...

We propose a motion and contrast enhancement separation model in dynamic magnetic resonance imaging (MRI). Furthermore, the reconstruction is done from partial measurements to achieve faster dynamic MR imaging. The algorithm minimizes a linear combination of three terms, a data fitting functional and two regularization functionals corresponding to the nuclear and l1 norm. The proposed method is...

1 Supplemental Material 1: Fitting Algorithm The estimation algorithm for the general model is a two-step (EM) iterative procedure to find the maximum of the likelihood of the observed data. The estimation algorithm iteratively computes (E-step) and maximizes (M-step) the expected likelihood of the complete data conditional on the observed data. The joint log-likelihood is l(α,β, σ ε , σ 2 s , ...

When a large body of data from diverse experiments is analyzed using a theoretical model with many parameters, the standard error matrix method and the general tools for evaluating the error may become inadequate. We present an iterative method that significantly improves the reliability, and hence the applicability, of the error matrix calculation. Also, to obtain more accurate estimates of th...

We describe 'Active Shape Models' which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets of labelled points. An initial estimate of the location of the model points in an image is improved by attempting ...

The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple exponential family distributions, including bin...