A New Region-based Active Contour Model for Object Segmentation

Abstract

We present a novel region-based active contour model that segments one or more image regions that are visually similar to an object of interest, said prior. The region evolution equation of our model is defined by a simple heuristic rule and it is not derived by minimizing an energy functional, as in the classic variational approaches. The prior and the evolving region are described by the probability density function (pdf) of a photometric feature, as color or intensity. The heuristic rule deforms an initial region of the image in order to equalize pointwise the pdfs of the prior and of the region. Such heuristic rule can be modeled by many mathematical monotonic decreasing functions, each defining an evolution equation for the initial image region. The choice of a particular function is remitted to the user, that in this way can even integrate a priori knowledge possibly useful to break down the computational charge of the method and to increase the detection accuracy. Here we propose two different evolution equations for the general purpose of prior detection without a priori information and we discuss empirically the performances of our model on real-world and synthetic datasets. These experiments show that our model is a valid alternative to the classic models.