Skeletonization and Reconstruction based on Graph Morphological Transformations
This work addresses a specific problem in image processing and computer vision for researchers in graph-based morphological analysis, but it appears incremental as it builds on existing node-based methods by shifting focus to edges.
The paper tackles the problem of multiscale shape skeletonization on pixel adjacency graphs by proposing novel edge-based graph morphological transformations, enabling the use of path-based methods like distance maps and IFT for skeletonization and reconstruction of infrared thermal images, and discusses the difficulty of deciding connectivity in graph skeletonization as raised by Maragos et al. (2013).
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.