Tire impressions image segmentation algorithm based on C-V model without re-initialization

In this paper, we present a new tire impressions image segmentation algorithm based on C-V model without re-initialization by introducing an internal energy term that penalizes the deviation of the level set function from a signed distance function into the C-V model. The proposed model can keep the approximately the level set function as a signed distance function during the curve evolution. The level set function can be initialized with general functions that are more efficient to construct and easier to use than the widely used signed distance function in practice and speed up the curve evolution. Therefore, the consuming time to compute a signed distance function from an initial curve in irregular shape is saved. The proposed algorithm has been applied to both printing and collected tire impressions images in the scene with promising results.