In this work we approach the problem of model comparison between skew families. For the univariate skew model, we measure the sensitivity of the skewness parameter using the L-1 distance between symmetric and asymmetric ...

Bayesian influence measures for linear regression models have been developed mostly for normal regression models with noninformative prior distributions for the unknown parameters. In this work we extend existing results ...

Poisson noise is the main cause of degradation of many imaging modalities. However, many of the proposed methods for reducing noise in images lack a formal approach. Our work develops a new, general, formal and computationally ...

Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al. [“Classification of segments in PolSAR imagery ...

In this paper we propose the zero-inflated COM-Poisson distribution. We develop a Bayesian analysis for our model via on Markov chain Monte Carlo methods. We discuss regression modeling and model selection, as well as, ...

The Euclidean distance transform (EDT) is used in various methods in pattern recognition, computer vision, image analysis, physics, applied mathematics and robotics. Until now, several sequential EDT algorithms have been ...

This paper presents an analytic framework to measure the spatial segregation caused by reducing or forbidding the free movement of pedestrians, due to the existence of a highway or other type of transport facility with ...

Cellular membranes have relevant roles in processes related to proteases like human kallikreins and cathepsins. As enzyme and substrate may interact with cell membranes and associated co-factors, it is important to take ...

This paper presents an algorithm to estimate lost bus voltage measurements in balanced distribution networks regulated by centralized control schemes. The objective is to feed controllers with reliable estimations of lost ...