论文标题
CATSIM：分类图像相似性度量
CatSIM: A Categorical Image Similarity Metric
论文作者
论文摘要
我们介绍了Catsim，这是一种新的相似性度量，用于二进制和多级两维图像和卷。 CATSIM使用结构相似性图像质量范式，并且对位置的小扰动具有鲁棒性，因此在相似但并非完全重叠的结构中，两个图像的区域比使用简单的匹配更高。指标还可以比较图像内的任意区域。对CATSIM进行了人工数据集，图像质量评估调查和两个成像应用的评估
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, regions of two images are rated higher than using simple matching. The metric can also compare arbitrary regions inside images. CatSIM is evaluated on artificial data sets, image quality assessment surveys and two imaging applications
