CORRECTION AND DENSIFICATION OF UAS-BASED PHOTOGRAMMETRIC THERMAL POINT CLOUD

Photogrammetric processing algorithms can suffer problems due to either the initial image quality (noise, low radiometric quality, shadows and so on) or to certain surface materials (shiny or textureless objects). This can result in noisy point clouds and/or difficulties in feature extraction. Specifically, dense point clouds which are generated with photogrammetric method using a lightweight thermal camera, are more noisy and sparse than the point clouds of high-resolution digital camera images. In this paper, new method which produces more reliable and dense thermal point cloud using the sparse thermal point cloud and high resolution digital point cloud was considered. Both thermal and digital images were obtained with UAS (Unmanned Aerial System) based lightweight Optris PI 450 and Canon EOS 605D camera images. Thermal and digital point clouds, and orthophotos were produced using photogrammetric methods. Problematic thermal point cloud was transformed to a high density thermal point cloud using image processing methods such as rasterizing, registering, interpolation and filling. The results showed that the obtained thermal point cloud - up to chosen processing parameters - was 87% more densify than the original point cloud. The second improvement was gained at the height accuracy of the thermal point cloud. New densified point cloud has more consistent elevation model while the original thermal point cloud shows serious deviations from the expected surface model

Exhaustive search procedure for multiple outlier detection

In the last decades many statistical tests based on the least squares solution have been proposed for multiple outlier detection. All of them suffer, however, from deficiencies that make them inefficient in their practical application. As recently demonstrated by the author, this situation is unavoidable in the framework of least squares theory. The present contribution elaborates on this impossibility of obtaining an unambiguous response for any statistical test based on the least squares solution and makes use of multiple least squares adjustments for statistically characterizing the equivalent sets of multiple gross error vectors. Several examples and a flexible Matlab implementation are provided.Baselga Moreno, S. (2011). Exhaustive search procedure for multiple outlier detection. Acta Geodaetica et Geophysica Hungarica. 46(4):401-416. doi:10.1556/AGeod.46.2011.4.3S401416464Baarda W 1968: A testing procedure for use in geodetic networks. Netherlands Geodetic Commission, Publications on Geodesy, New Series 2, No. 5, DelftBaselga, S. (2007). Global Optimization Solution of Robust Estimation. Journal of Surveying Engineering, 133(3), 123-128. doi:10.1061/(asce)0733-9453(2007)133:3(123)Baselga, S. (2011). Nonexistence of Rigorous Tests for Multiple Outlier Detection in Least-Squares Adjustment. Journal of Surveying Engineering, 137(3), 109-112. doi:10.1061/(asce)su.1943-5428.0000048Cen, M., Li, Z., Ding, X., & Zhuo, J. (2003). Gross error diagnostics before least squares adjustment of observations. Journal of Geodesy, 77(9), 503-513. doi:10.1007/s00190-003-0343-4Cross P A, Price D R 1984: A strategy for the detection of single and multiple gross errors in geodetic networks. In: Eighth UK Geophysical Assembly, Newcastle Upon TyneDing, X., & Coleman, R. (1996). Multiple outlier detection by evaluating redundancy contributions of observations. Journal of Geodesy, 70(8), 489-498. doi:10.1007/bf00863621Erenoglu, R., & Hekimoglu, S. (2010). Efficiency of robust methods and tests for outliers for geodetic adjustment models. Acta Geodaetica et Geophysica Hungarica, 45(4), 426-439. doi:10.1556/ageod.45.2010.4.3Eshagh, M., Sjöberg, L., & Kiamehr, R. (2007). Evaluation of robust techniques in suppressing the impact of outliers in a deformation monitoring network — a case study on the Tehran Milad tower network. Acta Geodaetica et Geophysica Hungarica, 42(4), 449-463. doi:10.1556/ageod.42.2007.4.6Gui, Q., Gong, Y., Li, G., & Li, B. (2007). A Bayesian approach to the detection of gross errors based on posterior probability. Journal of Geodesy, 81(10), 651-659. doi:10.1007/s00190-006-0132-yGuo, J.-F., Ou, J.-K., & Wang, H.-T. (2007). Quasi-Accurate Detection of Outliers for Correlated Observations. Journal of Surveying Engineering, 133(3), 129-133. doi:10.1061/(asce)0733-9453(2007)133:3(129)Hadi, A. S., & Simonoff, J. S. (1993). Procedures for the Identification of Multiple Outliers in Linear Models. Journal of the American Statistical Association, 88(424), 1264. doi:10.2307/2291266Huber, P. J. (1981). Robust Statistics. Wiley Series in Probability and Statistics. doi:10.1002/0471725250Knight, N. L., Wang, J., & Rizos, C. (2010). Generalised measures of reliability for multiple outliers. Journal of Geodesy, 84(10), 625-635. doi:10.1007/s00190-010-0392-

Population-based trend analysis of laparoscopic Nissen and Toupet fundoplications for gastroesophageal reflux disease

The Nissen and Toupet fundoplications are the most commonly used techniques for surgical treatment of gastroesophageal reflux disease. To date, no population-based trend analysis has been reported examining the choice of procedure and short-term outcomes. This study was designed to analyze trends in the use of Nissen versus Toupet fundoplications, and corresponding short-term outcomes during a 10-year period between 1995 and 2004