  Superpixel algorithms, which group pixels similar in color and other
low-level properties, are increasingly used for pre-processing in image
segmentation. Commonly important criteria for the computation of superpixels
are boundary adherence, speed, and regularity.
  Boundary adherence and regularity are typically contradictory goals. Most
recent algorithms have focused on improving boundary adherence. Motivated by
improving superpixel regularity, we propose a diagram-based superpixel
generation method called Power-SLIC.
  On the BSDS500 data set, Power-SLIC outperforms other state-of-the-art
algorithms in terms of compactness and boundary precision, and its boundary
adherence is the most robust against varying levels of Gaussian noise. In terms
of speed, Power-SLIC is competitive with SLIC.
