PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric

TL;DR

PointSSIM is a new low-dimensional, resolution-invariant image comparison metric that uses structural features and point pattern representations for robust image analysis across varying resolutions.

Contribution

It introduces a novel method combining structural similarity and morphological features to compare binary images of different resolutions effectively.

Findings

Provides an efficient, reliable image comparison method.

Effective for structural analysis across resolutions.

Outperforms existing metrics in robustness and efficiency.

Abstract

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.