A Level Line Selection Approach for Object Boundary Estimation

An energy model-based approach for estimating object boundaries is presented. We study a particular energy, which minimizer can be determined. The method estimates the unknown number of objects and draws object boundaries by selecting the “best” level lines computed from level sets of the original image. Unlike previous standard methods, the proposed method does not require iteration for minimizing the energy. In addition, our segmentation algorithm combines anisotropic diffusion-based regularization with level line selection to extract smooth object boundaries. Experimental results on 2D biomedical and meteorological images are reported.