A Note on Error Metrics and Optimization Criteria in 3D Vision
Matthias Mühlich, D. Feiden, Rudolf Mester · 1999
All computer vision algorithms applied to real data have to deal with input data corrupted by errors, which leads to the necessity to select an appropriate loss function and to minimize it. The fact that the errors in di#erent components can be of individual size or even be correlated, makes a statistical analysis of an algorithm absolutely necessary. A statistically justified loss function can only be obtained by a statistical consideration of errors, or, in other words, optimization criteria that are not statistically justified (i.e. criteria that use a wrong error metric) may result in drastic failure of an algorithm in the presence of errors.