As an image preprocessing technology, superpixel segmentation has become an important tool in the field of computer vision. How to obtain a more accurate, faster, and easier-to-apply superpixel segmentation algorithm is a problem faced by researchers. In this paper, a cluster-based fine-to-coarse superpixel segmentation (FCSS) algorithm is proposed. By introducing color thresholds and depth thresholds with practical physical meanings as algorithm parameters, high-quality segmentation with fewer superpixels is achieved. It not only reduces the complexity of the upper application, but also provides an easy to understand interface. Superpixel segmentation methods often cannot achieve high-quality segmentation through a set of parameters. Experimental results show that FCSS can achieve finer segmentation by setting different parameters, and the segmentation results are superior to other algorithms. When the number of superpixels is 100, the segmentation performance of FCSS is better than that of existing state-of-the-art methods.

作为图像预处理技术，超像素分割已成为计算机视觉领域的重要工具。如何获得更准确，更快，更易于应用的超像素分割算法是研究人员面临的问题。本文提出了一种基于聚类的细到粗超像素分割（FCSS）算法。通过引入具有实际物理意义的颜色阈值和深度阈值作为算法参数，可以实现具有较少超像素的高质量分割。它不仅降低了上层应用程序的复杂性，而且还提供了易于理解的界面。超像素分割方法通常无法通过一组参数实现高质量的分割。实验结果表明，通过设置不同的参数，FCSS可以实现更好的分割效果，分割结果优于其他算法。当超像素数为100时，FCSS的分割性能要优于现有的最新方法。