Parallel thinning and skeletonization algorithm based on cellular automaton

This paper proposes a parallel image thinning algorithm and a skeletonization algorithm based on cellular automaton (CA). Cellular automaton is a parallel computation model and a non-linear dynamical system. In this paper, each image pixel is identified as a cell of CA and the change of cell depends on the current state of itself and the state of its neighbors. In a binary image, this paper assumes that the objects (white pixel) are preys which are surrounded by many ants (every black pixels). The movement of ants is controlled by cellular automation. The ants gnaw preys until the preys (objects) become skeleton. The proposed parallel skeletonization algorithm can produce a traditional skeleton with a thin line located in the center of object, and the proposed thinning algorithm can produce a new kind of skeleton which is named as the ants-gnawing skeleton. The computation of ants-gnawing skeleton is faster than the traditional skeleton while it contains more the structural features of image. Benefiting from the properties of cellular automation, the proposed thinning algorithm does not change the basic geometry structure of image, and it is invariant for image rotation.