Skeletonization and Reconstruction based on Graph Morphological Transformations

TL;DR

This paper introduces a novel graph morphological transformation approach for skeletonization and reconstruction of infrared thermal images, focusing on edges and enabling path-based methods like distance maps and IFT.

Contribution

It proposes a new structured graph morphological transformation based on edges, expanding the capabilities of skeletonization techniques in image processing.

Findings

Enables use of path-based skeletonization methods like distance maps and IFT.

Discusses the complexity of graph skeleton connectivity as posed by Maragos et al.

Provides a framework for skeletonization on infrared thermal images.

Abstract

Multiscale shape skeletonization on pixel adjacency graphs is an advanced intriguing research subject in the field of image processing, computer vision and data mining. The previous works in this area almost focused on the graph vertices. We proposed novel structured based graph morphological transformations based on edges opposite to the current node based transformations and used them for deploying skeletonization and reconstruction of infrared thermal images represented by graphs. The advantage of this method is that many widely used path based approaches become available within this definition of morphological operations. For instance, we use distance maps and image foresting transform (IFT) as two main path based methods are utilized for computing the skeleton of an image. Moreover, In addition, the open question proposed by Maragos et al (2013) about connectivity of graph skeletonization method are discussed and shown to be quite difficult to decide in general case.