Previous work has suggested re-scaling data to a fixed size bounding box to avoid the basis-dependence issues.
In this article, it is proved that, under mild conditions, there exist group sequential tests which achieve the predesignated significance level and power with maximum total sample size bounded above by the sample size for the corresponding standard single-stage test.
These properties include bounded size, bounded diameter, connectivity.
Due to its low variance, we chose the relationship coefficient r from Li et al. [26], which is corrected for sample size, bounded by [−1; +1].
From the results, one can see that the ventricle parts are successfully recognized using size, bounding box and template constraints.
This statement will be addressed later in greater detail in the context of step-size bounds for stability.
This knowledge also provides the steady-state values and, furthermore, the derivation of practical step-size bounds.
In the case of very long filters, it turns out that even the mild approximations of the driving process are no longer required and thus less conservative step-size bounds are obtained.
Finally, it should be mentioned that neither do the deterministic approaches [4, 5, 7] require the IA in order to derive step-size bounds, but as in the case of other contributions, they are unable to provide learning curves or steady-state values without imposing the IA.
In particular, in the Weizmann dataset the fixed-size bounding boxes can be trivially extracted by considering a simple background subtraction algorithm.
