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Full-Text Articles in Physical Sciences and Mathematics
Autonomous Geometric Precision Error Estimation In Low-Level Computer Vision Tasks, Andrés Corrada-Emmanuel, Howard Schultz
Autonomous Geometric Precision Error Estimation In Low-Level Computer Vision Tasks, Andrés Corrada-Emmanuel, Howard Schultz
Computer Science Department Faculty Publication Series
Errors in map-making tasks using computer vision are sparse. We demonstrate this by considering the construction of digital elevation models that employ stereo matching algorithms to triangulate real-world points. This sparsity, coupled with a geometric theory of errors recently developed by the authors, allows for autonomous agents to calculate their own precision independently of ground truth. We connect these developments with recent advances in the mathematics of sparse signal reconstruction or compressed sensing. The theory presented here extends the autonomy of 3-D model reconstructions discovered in the 1990s to their errors.
Autonomous Estimates Of Horizontal Decorrelation Lengths For Digital Elevation Models, Andres Corrada-Emmanuel, Howard Schultz
Autonomous Estimates Of Horizontal Decorrelation Lengths For Digital Elevation Models, Andres Corrada-Emmanuel, Howard Schultz
Computer Science Department Faculty Publication Series
The precision errors in a collection of digital elevation models (DEMs) can be estimated in the presence of large but sparse correlations even when no ground truth is known. We demonstrate this by considering the problem of how to estimate the horizontal decorrelation length of DEMs produced by an automatic photogrammetric process that relies on the epipolar constraint equations. The procedure is based on a set of autonomous elevation difference equations recently proposed by us. In this paper we show that these equations can only estimate the precision errors of DEMs. The accuracy errors are unknowable since there is no …