Fig. 7 The executing times of MCWT-SG-O with various FFT radixes under scale = 30.
And again, our focus is on addressing recognition under scale changes, not just improving recognition rates.
The institutional architecture could vary from being top-down under scale economies to bottom up under scope economies.
We also discuss and evaluate different normalisation schemes that make the descriptors invariant under scale and rotation.
Many hierarchical structures generated at nano- and micro-scale have geometrical characteristics that are invariant under scale dilations, displaying self-similarity, and thus bearing fractal properties [1, 2].
Further, under scope economies, there would be optimal lower and upper limit to institutional architecture unlike the borderless view under scale economies.
Furthermore, test statistics should be invariant under scale transformations of the data, since the endpoints may be measured on different scales.
Most of the famous fractal sets (i.e. the Cantor set, the Von Koch curve, and the Sierpinski gasket) can be characterised by a single exponent that relates how their geometrical properties vary under scale changes.
