Adaptive smoothness based robust active contours

Abstract

Active contours are a popular class of variational models used in computer vision for tracking and segmentation. The variational model consists of a data-fitting and a regularisation term. Depending on the data-fitting term, active contour models are classified as either gradient or region based models. An often overlooked but crucial aspect of these models is that these two terms are weighted by a manually set constant weight. This constant weight often leads to incorrect segmentation, particularly for gradient based energies. This failure rate is high in the presence of strong gradients nearby the target or when the object gradient is not uniformly strong. In such circumstances, setting the weight becomes a critical and often unsatisfying task. In this work, we propose a new spatially varying and dynamic curve evolution term for robust segmentation of gradient based models. In contrast to the majority of the existing work in literature which focuses on defining new data-fitting terms, the evolution term proposed here is related to the regularisation of evolution. The intuition here is that in images although object boundaries are generally continuous, the magnitude of the gradient map so generated is not uniformly strong. Therefore, any energy formulation which fixes the weights of the data-fitting and regularisation term will run into the problems mentioned above. In this work, we propose an energy term which defines the regularisation term in a spatially varying manner. The advantage of this term is that it is independent of the image based data-fitting energy term and hence can be plugged into the vast variety of the existing gradient based active contour models.