提出一种不依赖分辨率的图像比对新方法，适用于不同尺寸二值图结构分析。
PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric
- 将二值图转为带标记点模式，用局部自适应极大值提取关键点
- 通过摘要向量捕捉强度、连通性、复杂度和结构特征实现比对
- 特别适合跨分辨率图像结构分析，如医学影像或遥感图
本文提出 PointSSIM，一种新颖的低维、分辨率不变的图像到图像比较度量。受结构相似性指数和数学形态学启发，PointSSIM 通过将二值图像转换为带标记点模式，实现对不同分辨率图像的鲁棒比对。关键特征点（即锚点）从二值图像中提取，方法是识别最小距离变换中的局部自适应极大值。图像比较基于一个摘要向量，该向量捕获强度、连通性、复杂度及结构属性。结果表明，该方法在跨分辨率结构分析任务中具有高效性和可靠性，尤其适用于需要精确结构匹配的应用场景。
原文摘要 · Abstract (English)
This paper presents PointSSIM, a novel low-dimensional image-to-image comparison metric that is resolution invariant. Drawing inspiration from the structural similarity index measure and mathematical morphology, PointSSIM enables robust comparison across binary images of varying resolutions by transforming them into marked point pattern representations. The key features of the image, referred to as anchor points, are extracted from binary images by identifying locally adaptive maxima from the minimal distance transform. Image comparisons are then performed using a summary vector, capturing intensity, connectivity, complexity, and structural attributes. Results show that this approach provides an efficient and reliable method for image comparison, particularly suited to applications requiring structural analysis across different resolutions.
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