Pareto optimality solution of the multi-objective photogrammetric resection-intersection problem

Reconstruction of architectural structures from photographs has recently experienced intensive efforts in computer vision research. This is achieved through the solution of nonlinear least squares (NLS) problems to obtain accurate structure and motion estimates. In Photogrammetry, NLS contribute to the determination of the 3-dimensional (3D) terrain models from the images taken from photographs. The traditional NLS approach for solving the resection-intersection problem based on implicit formulation on the one hand suffers from the lack of provision by which the involved variables can be weighted. On the other hand, incorporation of explicit formulation expresses the objectives to be minimized in different forms, thus resulting in different parametric values for the estimated parameters at non-zero residuals. Sometimes, these objectives may conflict in a Pareto sense, namely, a small change in the parameters results in the increase of one objective and a decrease of the other, as is often the case in multi-objective problems. Such is often the case with error-in-all-variable (EIV) models, e.g., in the resection-intersection problem where such change in the parameters could be caused by errors in both image and reference coordinates.This study proposes the Pareto optimal approach as a possible improvement to the solution of the resection-intersection problem, where it provides simultaneous estimation of the coordinates and orientation parameters of the cameras in a two or multistation camera system on the basis of a properly weighted multi-objective function. This objective represents the weighted sum of the square of the direct explicit differences of the measured and computed ground as well as the image coordinates. The effectiveness of the proposed method is demonstrated by two camera calibration problems, where the internal and external orientation parameters are estimated on the basis of the collinearity equations, employing the data of a Manhattan-type test field as well as the data of an outdoor, real case experiment. In addition, an architectural structural reconstruction of the Merton college court in Oxford (UK) via estimation of camera matrices is also presented. Although these two problems are different, where the first case considers the error reduction of the image and spatial coordinates, while the second case considers the precision of the space coordinates, the Pareto optimality can handle both problems in a general and flexible way

Projected Outcomes of Nurse-Family Partnership Home Visitation During 1996–2013, USA

Nurse-Family Partnership (NFP) targets intensive prenatal and postnatal home visitation by registered nurses to low-income first-time mothers. Through 2013, 177,517 pregnant women enrolled in NFP programs. This article projects how NFP will affect their lives and the lives of their babies. NFP has been evaluated in six randomized trials and several more limited analyses of operational programs. We systematically reviewed evaluation findings on 21 outcomes and calculated effects on three more. We added outcome data from the NFP national data system and personal communications that filled outcome data gaps on some trials. We assumed effectiveness in replication declined by 21.8 %, proportionally with the decline in mean visits per family from trials to operational programs. By 2031, NFP program enrollments in 1996–2013 will prevent an estimated 500 infant deaths, 10,000 preterm births, 13,000 dangerous closely spaced second births, 4700 abortions, 42,000 child maltreatment incidents, 36,000 intimate partner violence incidents, 90,000 violent crimes by youth, 594,000 property and public order crimes (e.g., vandalism, loitering) by youth, 36,000 youth arrests, and 41,000 person-years of youth substance abuse. They will reduce smoking during pregnancy, pregnancy complications, childhood injuries, and use of subsidized child care; improve language development; increase breast-feeding; and raise compliance with immunization schedules. They will eliminate the need for 4.8 million person-months of child Medicaid spending and reduce estimated spending on Medicaid, TANF, and food stamps by $3.0 billion (present values in 2010 dollars). By comparison, NFP cost roughly$1.6 billion. Thus, NFP appears to be a sound investment. It saves money while enriching the lives of participating low-income mothers and their offspring and benefiting society more broadly by reducing crime and safety net demand

Application of pareto optimality to linear models with errors-in-all-variables

In some geodetic and geoinformatic parametric modeling, the objectives to be minimized are often expressed in different forms, resulting in different parametric values for the estimated parameters at non-zero residuals. Sometimes, these objectives may compete in a Pareto sense, namely a small change in the parameters results in the increase of one objective and a decrease of the other, as frequently occurs in multiobjective problems. Such is the case with errors-in-all-variables (EIV) models, e.g., in the geodetic and photogrammetric coordinate transformation problems often solved using total least squares solution (TLS) as opposed to ordinary least squares solution (OLS). In this contribution, the application of Pareto optimality to solving parameter estimation for linear models with EIV is presented. The method is tested to solve two well-known geodetic problems of linear regression and linear conformal coordinate transformation. The results are compared with those from OLS, Reduced Major Axis Regression (TLS solution), and the least geometric mean deviation (GMD) approach. It is shown that the TLS and GMD solutions applied to the EIV models are just special cases of the Pareto optimal solution, since both of them belong to the Pareto-set of the problems. The Pareto balanced optimum (PBO) solution as a member of this Pareto optimal solution set has special features and is numerically equal to the GMD solution