  Many people see a human face or animals in the pattern of the maria on the
moon. Although the pattern corresponds to the actual variation in composition
of the lunar surface, the culture and environment of each society influence the
recognition of these objects (i.e., symbols) as specific entities. In contrast,
a convolutional neural network (CNN) recognizes objects from characteristic
shapes in a training data set. Using CNN, this study evaluates the
probabilities of the pattern of lunar maria categorized into the shape of a
crab, a lion and a hare. If Mare Frigoris (a dark band on the moon) is included
in the lunar image, the lion is recognized. However, in an image without Mare
Frigoris, the hare has the highest probability of recognition. Thus, the
recognition of objects similar to the lunar pattern depends on which part of
the lunar maria is taken into account. In human recognition, before we find
similarities between the lunar maria and objects such as animals, we may be
persuaded in advance to see a particular image from our culture and environment
and then adjust the lunar pattern to the shape of the imagined object.
