  Deep convolutional neural networks (CNNs) are widely known for their
outstanding performance in classification and regression tasks over
high-dimensional data. This made them a popular and powerful tool for a large
variety of applications in industry and academia. Recent publications show that
seemingly easy classifaction tasks (for humans) can be very challenging for
state of the art CNNs. An attempt to describe how humans perceive visual
elements is given by the Gestalt principles. In this paper we evaluate AlexNet
and GoogLeNet regarding their performance on classifying the correctness of the
well known Kanizsa triangles, which heavily rely on the Gestalt principle of
closure. Therefore we created various datasets containing valid as well as
invalid variants of the Kanizsa triangle. Our findings suggest that perceiving
objects by utilizing the principle of closure is very challenging for the
applied network architectures but they appear to adapt to the effect of
closure.
