Publication Date
July 2008
Journal or Book Title
International Conference on Machine Learning 2008
Abstract
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.
Recommended Citation
Corrada-Emmanuel, Andrés and Schultz, Howard, "Autonomous geometric precision error estimation in low-level computer vision tasks" (2008). International Conference on Machine Learning 2008. 2.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/2