Stripe Model: An Efficient Method to Detect Multi-form Stripe Structures

We present a general mathematical model for multiple forms of stripes. Based on the model, we propose a method to detect stripes built on scale-space. This method generates difference of Gaussian (DoG) maps by subtracting neighbor Gaussian layers, and reserves extremal responses in each DoG map by comparing to its neighbors. Candidate stripe regions are then formed from connected extremal responses. After that, approximate centerlines of stripes are extracted from candidate stripe regions using non-maximum suppression, which eliminates undesired edge responses simultaneously. And stripe masks could be restored from those centerlines with the estimated stripe width. Owing to the ability of extracting candidate regions, our method avoids traversing to do costly directional calculation on all pixels, so it is very efficient. Experiments show the robustness and efficiency of the proposed method, and demonstrate its ability to be applied to different kinds of applications in the image processing stage.