Automatic Distortion Rectification of WideAngle Images Using Outlier Refinement for Streamlining Vision Tasks The study proposes an outlier refinement methodology for automatic distortion rectification of wideangle and fisheye lens camera models in the context of streamlining visionbased tasks. The linemembers sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the linemembers set and update the robust line members as well as estimating the bestfit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wideangle and fisheye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixellevel (image quality, edge stretching effects, pixelpoint error) as well as higherlevel usecases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various selfcalibration approaches.

Efficient Algorithms for Global Outlier Removal in Largescale StructurefromMotion This work addresses the outlier removal problem in largescale global structurefrommotion. In such applications, outlier removal is very useful to mitigate the deterioration caused by mismatches in the feature point matching step. Unlike existing outlier removal methods, we exploit the structure in multiview geometry problems to propose a dimension reduced formulation, based on which two efficient methods have been developed. The first method considers a convex relaxed l 1  minimization and is solved by a single linear programming (LP). The second method approximately solves the ideal l 0  minimization by an iteratively reweighted method. The dimension reduction results in a significant speedup of the new algorithms. Further, the iteratively reweighted method can significantly reduce the possibility of removing true inliers. Results show that, compared with stateoftheart algorithms (e.g., the l 1  method), the proposed algorithms are more than three times faster and meanwhile produce better consensus sets. Matlab code for reproducing the results is available at https:github.comFWenOUTLR.git.