Bioinspired parallel 2D or 3D skeletonization

Algebraic Topology has been proved to be an useful tool to be used in image processing. In this case we will borrow some elements from Algebraic Topology in order to show a parallel algorithm for thinning a binary 3D image respecting its shape information. The parallelization of the thinning algorithm is based on Membrane Computing. This research area has already been proved to be useful in the development of parallel image processing algorithms. We present here the main guidelines of the algorithms along with a slight introduction about some basic required knowledge about Algebraic Topology and Membrane Computing

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

The use of modern information technology in research on transport accessibility

Transport accessibility can be analyzed using a number of different methods. The problem with each of them is the difficulty of obtaining data to measure this phenomenon The focus of this article and its main goal are to present methods and tools for gathering data on road traffic; thanks to modern information technology, it is possible to collect real data without the need for large-scale and highly capital-intensive measurements. The application of modern information technology (IT) presented in the article, such as computer programs and applications like Google Maps Traffic Overlay and TomTom Live Traffic, enable research to be conducted on a scale that has thus far been unattainable, and allows information to be collected on such criteria as traffic volume, flow, average traffic speed, and actual journey time. Such innovative means of gathering data on automobile traffic open up new perspectives for assessing transport accessibility in terms of automobile traffic by providing high-quality data that meet the requirements for use in primary research

Photogrammetric surveys and geometric processes to analyse and monitor red coral colonies

This article describes the set of photogrammetric tools developed for the monitoring of Mediterranean red coral Corallium rubrum populations. The description encompasses the full processing chain: from the image acquisition to the information extraction and data interpretation. The methods applied take advantage of existing tools and new, innovative and specific developments in order to acquire data on relevant ecological information concerning the structure and functioning of a red coral population. The tools presented here are based on: (i) automatic orientation using coded quadrats; (ii) use of non-photorealistic rendering (NPR) and 3D skeletonization techniques; (iii) computation of distances between colonies from a same site; and (iv) the use of a plenoptic approach in an underwater environment. © 2018 by the authors.This work is partially done in the framework of the PERfECT project, funded by the Foundation TOTAL, project 2014/257. The plenoptic camera was bought in the frame of the DGA RAPID LORI project (LOcalisation et Reconnaissance d’objets Immergés

Multi-Dimensional Medial Geometry: Formulation, Computation, and Applications

Medial axis is a classical shape descriptor. It is a piece of geometry that lies in the middle of the original shape. Compared to the original shape representation, the medial axis is always one dimension lower and it carries many intrinsic shape properties explicitly. Therefore, it is widely used in a large amount of applications in various fields. However, medial axis is unstable to the boundary noise, often referred to as its instability. A small amount of change on the object boundary can cause a dramatic change in the medial axis. To tackle this problem, a significance measure is often associated with the medial axis, so that medial points with small significance are removed and only the stable part remains. In addition to this problem, many applications prefer even lower dimensional medial forms, e.g., shape centers of 2D shapes, and medial curves of 3D shapes. Unfortunately, good significance measures and good definitions of lower dimensional medial forms are still lacking. In this dissertation, we extended Blum\u27s grassfire burning to the medial axis in both 2D and 3D to define a significance measure as a distance function on the medial axis. We show that this distance function is well behaved and it has nice properties. In 2D, we also define a shape center based on this distance function. We then devise an iterative algorithm to compute the distance function and the shape center. We demonstrate usefulness of this distance function and shape center in various applications. Finally we point out the direction for future research based on this dissertation

Medial Axis Approximation and Regularization

Medial axis is a classical shape descriptor. Among many good properties, medial axis is thin, centered in the shape, and topology preserving. Therefore, it is constantly sought after by researchers and practitioners in their respective domains. However, two barriers remain that hinder wide adoption of medial axis. First, exact computation of medial axis is very difficult. Hence, in practice medial axis is approximated discretely. Though abundant approximation methods exist, they are either limited in scalability, insufficient in theoretical soundness, or susceptible to numerical issues. Second, medial axis is easily disturbed by small noises on its defining shape. A majority of current works define a significance measure to prune noises on medial axis. Among them, local measures are widely available due to their efficiency, but can be either too aggressive or conservative. While global measures outperform local ones in differentiating noises from features, they are rarely well-defined or efficient to compute. In this dissertation, we attempt to address these issues with sound, robust and efficient solutions. In Chapter 2, we propose a novel medial axis approximation called voxel core. We show voxel core is topologically and geometrically convergent to the true medial axis. We then describe a straightforward implementation as a result of our simple definition. In a variety of experiments, our method is shown to be efficient and robust in delivering topological promises on a wide range of shapes. In Chapter 3, we present Erosion Thickness (ET) to regularize instability. ET is the first global measure in 3D that is well-defined and efficient to compute. To demonstrate its usefulness, we utilize ET to generate a family of shape revealing and topology preserving skeletons. Finally, we point out future directions, and potential applications of our works in real world problems

Towards the Fabrication Strategies for Intelligent Wire Arc Additive Manufacturing of Wire Structures from CAD Input to Finished Product

With the increasing demand for freedom of part design in the industry, additive manufacturing (AM) has become a vital fabrication process for manufacturing metallic workpieces with high geometrical complexity. Among all metal additive manufacturing technologies, wire arc additive manufacturing (WAAM), which uses gas metal arc welding (GMAW), is gaining popularity for rapid prototyping of sizeable metallic workpieces due to its high deposition rate, low processing conditions limit, and environmental friendliness. In recent years, WAAM has been developed synergistically with industrial robotic systems or CNC machining centers, enabling multi-axis free-form deposition in 3D space. On this basis, the current research of WAAM has gradually focused on fabricating strut-based wire structures to enhance its capability of producing low-fidelity workpieces with high spatial complexity. As a typical wire structure, the large-size free-form lattice structure, featuring lightweight, superior energy absorption, and a high strength-weight ratio, has received extensive attention in developing its WAAM fabrication process. However, there is currently no sophisticated WAAM system commercially available in the industry to implement an automated fabrication process of wire or lattice structures. The challenges faced in depositing wire structures include the lack of methods to effectively identify individual struts in wire structures, 3D slicing algorithms for the whole wire structures, and path planning algorithms to establish reasonable deposition paths for these generated discrete sliced layers. Moreover, the welded area of the struts within the wire structure is relatively small, so the strut forming is more sensitive and more easily affected by the interlayer temperature. Therefore, the control and prediction of strut formation during the fabricating process is still another industry challenge. Simultaneously, there is also an urgent need to improve the processing efficiency of these structures while ensuring the reliability of their forming result