Use Of Matching Techniques Research Articles

Comparison of Stereo Matching Algorithms for the Development of Disparity Map

Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG) and Fixed-Window Aggregated Cost (FWAC). In addition, three cost functions, namely, Mean Squared Error (MSE), Sum of Absolute Differences (SAD), and Normalized Cross-Correlation (NCC) were utilized and compared. The stereo images used in this study were obtained from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. It was observed that the selection of matching function is quite important and also depends on the image properties. Results showed that the BP algorithm in most cases provided better results achieving accuracies over 95%. Accordingly, BP algorithm is highly recommended based on rapidity and performance and for applications with the need of detection of small details, HOG is advised.

Use of matching methods in observational studies with critical patients and renal outcomes. Scoping review

Introduction: The use of matching techniques in observational studies has been increasing and is not always used appropriately. Clinical experiments are not always feasible in critical patients with renal outcomes, and observational studies are an important alternative.
 Objective: Through a scoping review, determine the available evidence on the use of matching methods in studies involving critically ill patients and assessing renal outcomes.
 Methods: Medline, Embase, and Cochrane databases were used to identify articles published between 1992 and 2020 up to week 10, which studied different exposures in the critically ill patient with renal outcomes and used propensity matching methods.
 Results: Most publications are cohort studies 94 (94. 9 %), five studies (5. 1 %) were cross-sectional. The main pharmacological intervention was the use of antibiotics in seven studies (7. 1%) and the main risk factor studied was renal injury prior to ICU admission in 10 studies (10. 1%). The balance between the baseline characteristics assessed by standardized means, in only 28 studies (28. 2%). Most studies 95 (96 %) used logistic regression to calculate the propensity index.
 Conclusion: Major inconsistencies were observed in the use of methods and in the reporting of findings. A summary is made of the aspects to be considered in the use of the methods and reporting of the findings with the matching by propensity index.

Non-Parametric Preprocessing for the Estimation of Equivalence Scales

Empirically analyzing household behavior usually relies on informal data preprocessing. That is, before an econometric model is estimated, observations are selected in such a way that the resulting subset of data can be assumed to be sufficiently homogeneous with respect to the specific research question pursued. For example, households with members above retirement age may be excluded where it seems important that they differ from other households with respect to time use and home production. We propose the use of matching techniques and balance checking at this initial stage. This can be interpreted as a non-parametric approach to preprocessing data and as a way to formalize informal procedures. To illustrate this, we use German micro-data on household expenditure to estimate equivalence scales as a specific example. Our results show that matching leads to results which are more stable with respect to model specification and that this type of formal preprocessing is especially useful if one is mainly interested in results for specific subgroups, such as low-income households.

Killing Kony

Is there a relationship between leadership change and the probability of conflict termination in civil war? This article uses an original data set on the leaders of rebel groups combined with existing data on state leaders to determine whether leadership change in states or rebel groups affects the probability that a civil war will end. Three results emerge: (1) when the leader of a rebel group is captured or killed, wars are 398 percent more likely to end, (2) conflicts are less likely to end while rebel groups are being led by their founder, and (3) the leader of a state that presided over the beginning of the conflict is significantly more likely to bring the conflict to an end than a replacement leader. The results are robust to the use of matching techniques and other tests of endogeneity.

Parties Getting Impatient: Time Out of Office and Portfolio Allocation in Coalition Governments

This article argues that long periods out of office make parties impatient and more willing to make concessions over portfolio allocation in exchange for participation in a coalition cabinet. Two hypotheses are analysed: on the one hand, being in opposition for a long time should put parties at a disadvantage when bargaining over office payoffs. On the other, this effect should not apply to the formateur party, since formation offers are based on the receivers’ impatience. The empirical results largely support these expectations. Additional evidence of the causality of the main effect is obtained through the use of matching techniques based on the propensity score.

Reassessing the forest impacts of protection

Protected areas are leading tools in efforts to slow global species loss and appear also to have a role in climate change policy. Understanding their impacts on deforestation informs environmental policies. We review several approaches to evaluating protection's impact on deforestation, given three hurdles to empirical evaluation, and note that “matching” techniques from economic impact evaluation address those hurdles. The central hurdle derives from the fact that protected areas are distributed nonrandomly across landscapes. Nonrandom location can be intentional, and for good reasons, including biological and political ones. Yet even so, when protected areas are biased in their locations toward less‐threatened areas, many methods for impact evaluation will overestimate protection's effect. The use of matching techniques allows one to control for known landscape biases when inferring the impact of protection. Applications of matching have revealed considerably lower impact estimates of forest protection than produced by other methods. A reduction in the estimated impact from existing parks does not suggest, however, that protection is unable to lower clearing. Rather, it indicates the importance of variation across locations in how much impact protection could possibly have on rates of deforestation. Matching, then, bundles improved estimates of the average impact of protection with guidance on where new parks’ impacts will be highest. While many factors will determine where new protected areas will be sited in the future, we claim that the variation across space in protection's impact on deforestation rates should inform site choice.