Research on Geodesic Active Contour Model for Fast Robust Segmentation of Complex Image

The precision of grinding and positioning by industry robot is influenced by segmentation accuracy of region of interest (ROI). The inherent defects of casting billet including perforation defects, crack defects, surface defects, and the polluted environment of post-processing may cause poor imaging quality which may have adverse effects on image segmentation. Considering the need for localization and identification in casting post-processing production line, this paper proposed a novel active contour algorithm which is suitable for the segmentation of complex images. First of all, the penalty term based on the local gradient information of image was introduced into the energy function, which will modify the deviation between evolution model of level set and Signed Distance Function (SDF) to reduce repeated initialization and improve convergence accuracy. Then, Gaussian pyramid energy term is incorporated into the energy function of geodesic active contour model to further accelerate its convergence speed. Finally, iteratively calculate the energy function of the proposed level set model to realize the evolution of contour curve, which can realize fast robust segmentation of complex image by solving the zero level set of the energy function. The comparative experiments between conventional method and the method proposed in this paper prove that the proposed method has higher processing speed than traditional C-V model on condition that the segmentation precision is guaranteed.