Heuristic method based on voting for extrinsic orientation through image epipolarization

[EN] Traditionally, the stereo-pair rectification, also known as epipolarization problem, (i.e., the projection of both images onto a common image plane) is solved once both intrinsic (interior) and extrinsic (exterior) orientation parameters are known. A heuristic method is proposed to solve both the extrinsic orientation problem and the epipolarization problem in just one single step. The algorithm uses the main property of a coplanar stereopair as fitness criteria: null vertical parallax between corresponding points to achieve the best stereopair. Using an iterative approach, each pair of corresponding points will vote for a rotation axis that may reduce vertical parallax. The votes will be weighted, the rotation applied, and an iteration will be carried out, until the vertical parallax residual error is below a threshold. The algorithm performance and accuracy are checked using both simulated and real case examples. In addition, its results are compared with those obtained using a traditional nonlinear least-squares adjustment based on the coplanarity condition. The heuristic methodology is robust, fast, and yields optimal results.The authors gratefully acknowledge the support from the Spanish Ministerio de Economia y Competitividad to the Project No. HAR2014-59873-R.Martín, S.; Lerma García, JL.; Uzkeda, H. (2017). Heuristic method based on voting for extrinsic orientation through image epipolarization. Journal of Electronic Imaging. 26(6):063020-1-063020-11. https://doi.org/10.1117/1.JEI.26.6.063020S063020-1063020-1126

Using AI tools to fill an incomplete well log dataset: A workflow

One issue commonly found when working with well log data is the irregular abundance/availability of the different recorded parameters. This is especially applicable when working with datasets collected in different campaigns that may span through the years, even decades, or different companies. Artificial Intelligence may be useful to fill gaps in the original database, resulting in a more complete, standardised one. In this work we present a workflow that can be followed to fills gaps in a dataset using different AI techniques. It consists of four main steps: 1) feature combination selection; 2) hyperparameter tuning; 3) performance assessment and best option choice; 4) blind testing. The process can be performed iteratively, successively populating the database with missing parameters, starting with those for which there are more available training data and whose results are more reliable. In this work, we present an example in which we filled an incomplete dataset consisting of wells provided by the UK National Data Repository (NDR) of the Oil & Gas Authority (OGA). The performance of some of the most commonly used artificial intelligence methods (support vector machine, random forest, multi-layer perceptron) was tested varying their hyperparameters until reaching an adequate result

Effects of inherited structures on inversion tectonics: Examples from the Asturian Basin (NW Iberian Peninsula) interpreted in a Computer Assisted Virtual Environment (CAVE)

Map shows mid-nineteenth century Texas counties, major cities, towns, roads, railroads, and areas of Native American habitation. Includes detailed notes on map. Insets: "Plan of Sabine Lake," "Plan of the Northern Part of Texas," and "Plan of Galveston Bay." Relief shown by hachures. Depths shown by soundings on inset. Scales [ca. 1:2,350,000], [ca. 1: 529,000], [ca. 1:3,800,000], and [ca. 1:887,000]