Consideration of uncertainty in computer vision: Necessity and chance

Observations and decisions in computer vision are inherently uncertain. The rigorous treatment of uncertainty has therefore received a lot of attention, since it not only improves the results compared to ad hoc methods but also makes the results more explainable. In this paper, the usefulness of stochastic approaches will be demonstrated by example with selected problems. These are given in the context of optimal estimation, self-diagnostics, and performance evaluation and cover all steps of the reasoning chain. The removal or interpretation of unexplainable thresholds and tuning parameters will be discussed for typical tasks in feature extraction, object reconstruction, and object classification. The text was submitted by the author in English. Jochen Meidow studied surveying and mapping at the University of Bonn, Germany, and graduated with a diploma in 1996. As research associate at the Institute for Theoretical Geodesy, University of Bonn, he received his PhD degree (Dr.-Ing.) in 2001 for a thesis about aerial image analysis. Between 2001 and 2004 he was a postdoctoral fellow at the Institute for Photogrammetry, University of Bonn, and since 2004 he is with the Research Institute for Optronics and Pattern Recognition (FGAN-FOM) in Ettlingen, Germany. He is a member of the DAGM (German Pattern Recognition Society). His research interests are adjustment theory, statistics, and spatial reasoning.