Detecting edges in images which are distorted by unreliable or missing data samples can be done using normalized (differential) convolution. This work presents a comparison between gradient estimation using normalized convolution and gradient estimation using normalized differential convolution with regard to speed, accuracy, and noisesensitivity.

In this paper, we address the problem of tracking the temporal evolution of arbitrary shapes observed in multi-camera setups. This is motivated by the ever growing number of applications that require consistent shape information along temporal sequences. The approach we propose considers a temporal sequence of independently reconstructed surfaces and iteratively deforms a reference mesh to fit ...

Suppose that a random variable X of interest is grouped or censored or missing so that one only observes a coarsening of X, i.e., a random set containing X with probability 1. It is assumed that the coarsening mechanism has the coarsening at random property. Suppose furthermore that the coarsening either equals X itself or that is a set with positive Xprobability. We modify the NPMLE of the dis...

laborious. In particular, under congested conditions, manual processing may be required, since automated approaches fail to identify the vehicles reliably. As a result, there may be measurement errors as well as missing data points in the extracted data set. A method is proposed here to perform the task of extracting useful information from position data efficiently while the ability to recover...

Abstract. The paper presents a novel algorithm for restoration of the missing samples in additive Gaussian noise based on the forward–backward autoregressive (AR) parameter estimation approach and the extrapolation technique. The proposed algorithm is implemented in two consecutive steps. In the first step, the forward–backward approach is used to estimate the parameters of the given neighbouri...

Rough sets methodology is a useful tool for analysis of decision problems concerning a set of objects described in a data table by a set of condition attributes and by a set of decision attributes. In practical applications, however, the data table is often not complete because some data are missing. To deal with this case, we propose an extension of the rough set methodology to the analysis of...

Conjoint analysis seeks to explain an ordered categorical ordinal variable according to several variables using a multiple regression scheme. A common problem encountered, there, is the presence of missing values in classification-ranks. In this paper, we are interested in the cases where consumers provide a ranking of some products instead of rating these products (i.e. explained variable pres...

This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptotic behavior of complete case versions of established full data methods without lengthy proofs. The methodology is illustrated by analyzing three in...

In this paper, we compare alternative missing imputation methods in the presence of ordinal data, in the framework of CUB (Combination of Uniform and (shifted) Binomial random variable) models. Various imputation methods are considered, as are univariate and multivariate approaches. The first step consists of running a simulation study designed by varying the parameters of the CUB model, to con...

Suppose that a random variable X of interest is grouped or censored or missing so that one only observes a coarsening of X, i.e., a random set containing X with probability 1. It is assumed that the coarsening mechanism has the coarsening at random property. Suppose furthermore that the coarsening either equals X itself or that is a set with positive X-probability. We modify the NPMLE of the di...