Geodesic grassfire for computing mixed-dimensional skeletons

Skeleton descriptors are commonly used to represent, understand and process shapes. While existing methods produce skeletons at a fixed dimension, such as surface or curve skeletons for a 3D object, often times objects are better described using skeleton geometry at a mixture of dimensions. In this paper we present a novel algorithm for computing mixed-dimensional skeletons. Our method is guided by a continuous analogue that extends the classical grassfire erosion. This analogue allows us to identify medial geometry at multiple dimensions, and to formulate a measure that captures how well an object part is described by medial geometry at a particular dimension. Guided by this analogue, we devise a discrete algorithm that computes a topology-preserving skeleton by iterative thinning. The algorithm is simple to implement, and produces robust skeletons that naturally capture shape components. Under Revie

NEFI: Network Extraction From Images

Networks and network-like structures are amongst the central building blocks of many technological and biological systems. Given a mathematical graph representation of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to graphs describing large scale networks such as social networks, protein-interaction networks, etc. In these applications, graph acquisition, i.e., the extraction of a mathematical graph from a network, is relatively simple. However, for many network-like structures, e.g. leaf venations, slime molds and mud cracks, data collection relies on images where graph extraction requires domain-specific solutions or even manual. Here we introduce Network Extraction From Images, NEFI, a software tool that automatically extracts accurate graphs from images of a wide range of networks originating in various domains. While there is previous work on graph extraction from images, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners from many disciplines to easily extract graph representations from images by supplying flexible tools from image processing, computer vision and graph theory bundled in a convenient package. Thus, NEFI constitutes a scalable alternative to tedious and error-prone manual graph extraction and special purpose tools. We anticipate NEFI to enable the collection of larger datasets by reducing the time spent on graph extraction. The analysis of these new datasets may open up the possibility to gain new insights into the structure and function of various types of networks. NEFI is open source and available http://nefi.mpi-inf.mpg.de