A Snake Model Driven by Dynamic Local Data

There are several drawbacks in the existing localization (active contour) models. Some models have poor capability of handling uneven illuminations and low contrasts, others are sensitive to initial condition, and there are still some models which can not converge to the object boundary stably. In this paper, following the routes of Localizing Region-Based Active Contours (LRBAC) which is an important localization method, we propose a new localized active contour model. By altering the underlying construction logic, our proposed algorithm overcomes the problem of LRBAC with respect to poor convergence stability. Compared with some state-of-the-art localization models, our new algorithm is more similar to an edge-based one and therefore performs better when handling uneven illuminations and low contrasts. Moreover, combining the features of the region-based and the edge-based active contours, we propose, for our algorithm, a simple approach to dynamically control the localization size. This dynamical method makes our algorithm more robust to the initial condition. Detailed theoretical analysis and comparison are presented to clarify the features of our proposed algorithm. Experimental results on real-image segmentation underline the effectiveness of our proposed algorithm.