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Methods to register two sets of data have existed for quite some time. However, these sets of data are rarely error-free. Consequently, any registration based on this data will be affected by the error. Moreover, if the corresponding registration matrix is then used to transform data from one coordinate system to another, any error from the registration will also get propagated to the transformed data. In this paper, we will characterize this propagation of random error, or noise, through a mathematical perspective and will illustrate its use with data obtained from physical experiments and with quasi-simulated sets of data. In addition, we will discuss the limitations of this propagation of error when systematic bias is present in the data.

Propagation of Error from Registration Parameters to Transformed Data