Comparison of Data with Multiple Degrees of Freedom Utilizing the Feature Selective Validation Method

The feature selective validation method has been shown to provide results that are in broad agreement with the visual assessment of a group of engineers for line, 1-D, data. An implementation using 2-D Fourier transforms and derivatives have been available for some years, but verification of the performance has been difficult to obtain. Further, that approach does not naturally scale well for 3-D and higher degrees of freedom, particularly if there are sizable differences in the number of points in the different directions. This paper describes an approach based on repeated 1-D FSV analyses that overcomes those challenges. The ability of the 2-D case to mirror user perceptions is demonstrated using the LIVE database. Its extension to n-dimensions is also described and includes a suggestion for weighting the algorithm based on the number of data points in a given "direction."