superpixel-benchmark
This repository provides a detailed evaluation of 28 superpixel algorithms utilizing 5 datasets to assess visual quality, performance, and robustness. It acts as a supplemental resource for a comparison published in Computer Vision and Image Understanding, 2018. Key updates include Docker implementations and evaluations of average metrics. The repository allows for fair benchmarking by optimizing parameters on separate training sets, focusing on metrics such as Boundary Recall and Undersegmentation Error.
