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Incorporating scale invariance into the cellular associative neural network

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Fig. 1. (a) The “Corner Turning 2” CANN module configuration, where S is the cellstate (the output of the combiner module) after each iteration and (b) information flowof this configuration over two iterations [13].
Fig. 3. (a) Cellular grid used when extracting primitives from Shape C and (b) theinput image closest to 100% of the original size of Shape C, for each resized versionfrom C25 to C200.
Table 1. Error rates of the scale invariant CANN, when recalling Shapes A–H pre-sented at scales ranging from 25% to 200%

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