  In contrast to human vision, common recognition algorithms often fail on
partially occluded images. We propose characterizing, empirically, the
algorithmic limits by finding a minimal recognizable patch (MRP) that is by
itself sufficient to recognize the image. A specialized deep network allows us
to find the most informative patches of a given size, and serves as an
experimental tool. A human vision study recently characterized related (but
different) minimally recognizable configurations (MIRCs) [1], for which we
specify computational analogues (denoted cMIRCs). The drop in human decision
accuracy associated with size reduction of these MIRCs is substantial and
sharp. Interestingly, such sharp reductions were also found for the
computational versions we specified.
