An Active Contour Model Based on Fuzzy Superpixel Centers and Nonlinear Diffusion Filter for Instance Segmentation

The significant weaknesses of the active contour model (ACM) are the manual setting of contour and the inability to process images with complex information, which limits its efficiency and application scope. In this article, an ACM, called FSC&NDF, is combined with fuzzy superpixel centers (FSCs) and nonlinear diffusion filter (NDF) to solve the above two problems simultaneously. YOLOv9 is adopted to locate the superpixels of interest; the joint boundaries of these superpixels are set as the initial contour, which is close to the morphological features of the target. Improved fuzzy superpixel clustering is applied to extract image features and yield superpixel centers, and the clusters are integrated into the main body of the energy function, NDF module further enhances boundary positioning and suppresses noise. In addition, the proposed connection mechanism makes it possible to convert object detection to instance segmentation. Experimental results show that FSC&NDF overcomes the limitations of previous ACMs in all aspects and its FPS, AP, AP50, and $\mathrm {AP}_{M}$ are higher than mainstream deep learning algorithms. The platform experiment based on the telecentric lens further proves the practicality of FSC&NDF.