A computer vision pipeline was assembled, whose purpose is to detect square-shaped regions of relative high contrast lying on the diagonal of reshuffled similarity matrices (SI Fig. ). SI Fig. shows a step-by-step visualisation of an example computation. CV consists of a) replacement of the values on the diagonal by the average value of the pixels immediately to the left and right to shadow the overly high values in this zone which obscure the observation of square-like shapes. In b), standard procedures of upsampling, blurring and padding are applied to remove noise and increase resolution of the images as a preparation for next step. In c), Canny's algorithm for detecting edges is applied , and in d) shapes are detected on the resulting images. These shapes are then filtered for squares lying on the diagonal, whose size corresponds to no more than 20 elements, that is 20 pixels in the original unprocessed matrix. The method depends on four parameters in total and the following assignments were found to provide good results: up-sampling factor = 15, Blurring window size any of {17, 19, 21, 23}, and two parameters associated with Canny's algorithm, which set the different thresholds as explained in are chosen so that th + b = 40, and th is sampled with a probability given by a Boltzmann distribution on th with parameter β = 20.