CatSIM：一种分类图像相似性度量
CatSIM: A Categorical Image Similarity Metric
AI总结：
本文提出CatSIM，一种基于结构相似性且对位置扰动鲁棒的分类图像相似性度量，可比较任意区域，经人工感知验证，并提供R包实现。
摘要：
我们提出CatSIM，一种用于二值和多值二维及三维图像和体积的新相似性度量。CatSIM采用结构相似性图像质量范式，并对位置的小扰动具有鲁棒性，使得相似但并非完全重叠的图像或体积区域中的结构比简单匹配获得更高的评分。该度量还可以比较图像和体积内部的任意区域。CatSIM在人工数据集上进行了评估，通过两项独立的图像质量评估调查与人类感知进行比较来验证，并在两个数据集上进行了展示。公开可用的R包“catsim”实现了该方法。
英文摘要：
We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes. CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, image or volumetric regions are rated higher than by simple matching. The metric can also compare arbitrary regions inside images and volumes. CatSIM is evaluated on artificial data sets, validated by comparing with human perception in two separate image quality assessment surveys, and illustrated on two datasets. The publicly available R package \texttt{catsim} implements the methodology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
