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

Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System

15th International Conference on Enterprise Information Systems, ICEIS 2013, France, 4-7 July 2013Logistic regression (LR) and naïve Bayes (NB) extensively used for prediction of fault-proneness assume linear addition and independence that often cannot hold in practice. Hence, we propose a Bayesian network (BN) model with incorporation of data mining techniques as an integrative approach. Compared with LR and NB, BN provides a flexible modeling framework, thus avoiding the corresponding assumptions. Using the static metrics such as Chidamber and Kemerer’s (C-K) suite and complexity as predictors, the differences in performance between LR, NB and BN models were examined for fault-proneness prediction at the class level in continual releases (five versions) of Rhino, an open-source implementation of JavaScript, developed using the agile process. By cross validation and independent test of continual versions, we conclude that the proposed BN can achieve a better prediction than LR and NB for the agile software due to its flexible modeling framework and incorporation of multiple sophisticated learning algorithms.Department of Computin