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Autonomous Geometric Precision Error Estimation in Low-level Computer Vision Tasks
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
