  Widely employed in cognitive psychology, Gestalt theory elucidates basic
principles in visual perception, but meanwhile presents significant challenges
for computation. The advancement of artificial intelligence requires the
emulation of human cognitive behavior, for which Gestalt theory serves as a
fundamental framework describing human visual cognitive behavior. In this
paper, we utilize persistent homology, a mathematical tool in computational
topology, to develop a computational model for Gestalt theory, addressing the
challenges of quantification and computation. The Gestalt computational model
not only holds promise for applications in artificial intelligence and computer
vision, but also opens a new research direction of computational visual
perception.
