Computes the similarity between a grid graph template and a grid graph probe
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
|The Gabor jet similarity function to be used
|ScalarProduct, Canberra, Disparity, PhaseDiff, PhaseDiffPlusCanberra
The code for this algorithm in Python
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In this algorithm, the similarity between a template graph T (which is a concatenation of several enrollment graphs) and a probe sample P is computed. The similarities of all node positions n is simply averaged:
In each node, the similarity of all enrollment jets tm with the probe jet p is computed, and the maximum value is taken:
Where S is a Gabor jet similarity function, which can be chosen accordingg to [Guenther12].
|Manuel Günther, Denis Haufe, Rolf P. Würtz. Face recognition with disparity corrected Gabor phase differences. Artificial Neural Networks and Machine Learning, pp. 411-418, 2012.