The method extracts useful information from a complicated signal where the overlap among the harmonics can raise up to 30%.
The spectral moment method extracts the information on the principal axis and its ratio to the minor axis automatically from the second-order moment calculations.
This implies that the proposed method extracts essential information from the degraded iris images and this capability indicates the effectiveness of our method for less constrained image capture setups like what happens in mobile electronic devices.
The spectral moment method extracts the information about the shift velocity (U_{mathrm{sft}}) and the sweeping velocity (U_{mathrm{swp}}) from the measurement of the spectrum (E k_{mathrm{s, omega }})).
EMRlog method extracts this information from the EMR database log using C4.5 algorithm.
Their method, which extracts information on the relative durations of the glottal opening and closing phases from inverse filtered speech, performs very effective classification for a single-dimension feature, when evaluated on a six-emotion-acted speech database.
The MI method, which extracts information about the partly missing covariate from the dependent variable, is hence insensitive to η-shrinkage even though the individual estimates, used in the imputations, suffer from shrinkage.
Each method extracted relevant information but had distinct limitations (i.e., temporal and frequency resolutions).
Of course our method extracts far more information than the single value obtained via a worm sorter.
