Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Prediction with measurement errors in finite populations

We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors. (C) 2011 Elsevier By. All rights reserved.

Influence Assessment in an Heteroscedastic Errors-in-Variables Model

The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.

Homology and errors

A recent review of the homology concept in cladistics is critiqued in light of the historical literature. Homology as a notion relevant to the recognition of clades remains equivalent to synapomorphy. Some symplesiomorphies are homologies inasmuch as they represent synapomorphies of more inclusive taxa; others are complementary character states that do not imply any shared evolutionary history among the taxa that exhibit the state. Undirected character-state change (as characters optimized on an unrooted tree) is a necessary but not sufficient test of homology, because the addition of a root may alter parsimonious reconstructions. Primary and secondary homology are defended as realistic representations of discovery procedures in comparative biology, recognizable even in Direct Optimization. The epistemological relationship between homology as evidence and common ancestry as explanation is again emphasized. An alternative definition of homology is proposed. (c) The Willi Hennig Society 2012.

A log-Birnbaum-Saunders regression model with asymmetric errors

This paper introduces a skewed log-Birnbaum-Saunders regression model based on the skewed sinh-normal distribution proposed by Leiva et al. [A skewed sinh-normal distribution and its properties and application to air pollution, Comm. Statist. Theory Methods 39 (2010), pp. 426-443]. Some influence methods, such as the local influence and generalized leverage, are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.

Biometry and fetal weight estimation by two-dimensional and three-dimensional ultrasonography: an intraobserver and interobserver reliability and agreement study

Objective To evaluate and compare the intraobserver and interobserver reliability and agreement for the biparietal diameter (BPD), abdominal circumference (AC), femur length (FL) and estimated fetal weight (EFW) obtained by two-dimensional ultrasound (2D-US) and three-dimensional ultrasound (3D-US). Methods Singleton pregnant women between 24 and 40 weeks were invited to participate in this study. They were examined using 2D-US in a blinded manner, twice by one observer, intercalated by a scan by a second observer, to determine BPD, AC and FL. In each of the three examinations, three 3D-US datasets (head, abdomen and thigh) were acquired for measurements of the same parameters. We determined EFW using Hadlock's formula. Systematic errors between 3D-US and 2D-US were examined using the paired t-test. Reliability and agreement were assessed by intraclass correlation coefficients (ICCs), limits of agreement (LoA), SD of differences and proportion of differences below arbitrary points. Results We evaluated 102 singleton pregnancies. No significant systematic error between 2D-US and 3D-US was observed. The ICC values were higher for 3D-US in both intra- and interobserver evaluations; however, only for FL was there no overlap in the 95% CI. The LoA values were wider for 2D-US, suggesting that random errors were smaller when using 3D-US. Additionally, we observed that the SD values determined from 3D-US differences were smaller than those obtained for 2D-US. Higher proportions of differences were below the arbitrarily defined cut-off points when using 3D-US. Conclusion 3D-US improved the reliability and agreement of fetal measurements and EFW compared with 2D-US.

Utilization of fringe projection technique for evaluation of wound dimensions and of healing progress.

Background: The methods used for evaluating wound dimensions, especially the chronic ones, are invasive and inaccurate. The fringe projection technique with phase shift is a non-invasive, accurate and low-cost optical method. Objective: The aim is to validate the technique through the determination of dimensions of objects of known topography and with different geometries and colors to simulate the wounds and tones of skin color. Taking into account the influence of skin wound optical factors, the technique will be used to evaluate actual patients’ wound dimensions and to study its limitations in this application. Methods: Four sinusoidal fringe patterns, displaced ¼ of period each, were projected onto the objects surface. The object dimensions were obtained from the unwrapped phase map through the observation of the fringe deformations caused by the object topography and using phase shift analysis. An object with simple geometry was used for dimensional calibration and the topographic dimensions of the others were determined from it. After observing the compatibility with the data and validating the method, it was used for measuring the dimensions of real patients’ wounds. Results and Conclusions: The discrepancies between actual topography and dimensions determined with Fringe Projection Technique and for the known object were lower than 0.50 cm. The method was successful in obtaining the topography of real patient’s wounds. Objects and wounds with sharp topographies or causing shadow or reflection are difficult to be evaluated with this technique.