Region Competition Based Active Contour for Image Partitioning

In this paper, we propose an image partitioning method using level set evolution for an arbitrary number of regions and embark on the concept of using one level set function for each region. The energy functional of each level set uses shifted Heaviside functions to obtain a stationary global minimum, which makes the proposed algorithm invariant to the level set initialization. In addition, unlike most of the previous works, the curve evolution partial differential equations for different level set equations are decoupled by applying the min operator and the proposed algorithm allows the effective number of regions to vary during the evolving process. Each region of class evolves according to its features and competes with the neighbor regions in order to get a partition. Generally, the proposed algorithm is fast, easy to implement, and not sensitive to the choice of initial conditions. Results are shown on both synthetic and real images.