Multiscale Centerline Detection

Finding the centerline and estimating the radius of linear structures is a critical first step in many applications, ranging from road delineation in 2D aerial images to modeling blood vessels, lung bronchi, and dendritic arbors in 3D biomedical image stacks. Existing techniques rely either on filters designed to respond to ideal cylindrical structures or on classification techniques. The former tend to become unreliable when the linear structures are very irregular while the latter often has difficulties distinguishing centerline locations from neighboring ones, thus losing accuracy. We solve this problem by reformulating centerline detection in terms of a \emph{regression} problem. We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes. The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified. We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets. Moreover, our approach is very generic and also performs well on contour detection. We show an improvement above recent contour detection algorithms on the BSDS500 dataset

Lidar-Derived Forest Metrics Are Critical for Predicting Snow Accumulation and Ablation in the Central Sierra Nevada, USA

Snowmelt is a critical source of water resources for ecosystems and communities surrounding the Sierra Nevada. Forest canopy controls critical mass and energy balance dynamics that can alter snowpack accumulation and ablation. In addition to changing climate dynamics that could shift the precipitation regimes of this region, an increase in ecosystem disturbance (e.g., drought, wildfires) creates dynamic forest structures that have the ability to drastically alter the snowpack. Forest management aims at creating resilient ecosystems but is often less explicitly focused on retaining the snowpack as a crucial water reservoir. It is important to constrain how fine-scale forest structures impact snowpack accumulation and persistence to predict future dynamics and inform management. However, while broad-scale forest structure metrics have been studied extensively in relation to snowpack, less is known about how fine-scale forest structure impacts snowpack. Light detection and ranging (lidar) data provide the opportunity to understand these complex dynamics using high resolution, spatially distributed points that capture detailed forest structure and snow depth. We use lidar collected over the course of multiple accumulation seasons both pre- and post-disturbance in Sagehen Creek Basin in the central Sierra Nevada to investigate how snowpack accumulation is impacted by fine-scale forest structure metrics, like leaf area index (LAI’) and the ratio of gap width to average tree height (openness) in a 30-meter grid cell. In addition, we use a series of measurements taken during the ablation season to understand how forest structures impact snow persistence. Through developing a refined space-for-structure processing protocol, we show a delicate balance between the fraction of forest cover (fVEG) and openness in an area that promotes snowpack accumulation and reduces ablation. In general, areas with lower fVEG (0.3) and smaller gaps (diameter/height ~0.1) increase accumulation. However, decreasing gap sizes and increasing fVEG can also lead to more ablation, supporting climate-driven paradigms that predict more ablation under the canopy in regions like the Sierra Nevada. Pre- and post-disturbance analyses show inconsistent patterns because of confounding accumulation and ablation dynamics at the date of collection. Our processing protocol and space-for-structure analysis provide a unique opportunity to understand lidar-derived forest-snow dynamics in a way that is transferrable to areas with varying vegetation and climate regimes

Detection of hidden structures on all scales in amorphous materials and complex physical systems: basic notions and applications to networks, lattice systems, and glasses

Recent decades have seen the discovery of numerous complex materials. At the root of the complexity underlying many of these materials lies a large number of possible contending atomic- and larger-scale configurations and the intricate correlations between their constituents. For a detailed understanding, there is a need for tools that enable the detection of pertinent structures on all spatial and temporal scales. Towards this end, we suggest a new method by invoking ideas from network analysis and information theory. Our method efficiently identifies basic unit cells and topological defects in systems with low disorder and may analyze general amorphous structures to identify candidate natural structures where a clear definition of order is lacking. This general unbiased detection of physical structure does not require a guess as to which of the system properties should be deemed as important and may constitute a natural point of departure for further analysis. The method applies to both static and dynamic systems.Comment: (23 pages, 9 figures

Structures in magnetohydrodynamic turbulence: detection and scaling

We present a systematic analysis of statistical properties of turbulent current and vorticity structures at a given time using cluster analysis. The data stems from numerical simulations of decaying three-dimensional (3D) magnetohydrodynamic turbulence in the absence of an imposed uniform magnetic field; the magnetic Prandtl number is taken equal to unity, and we use a periodic box with grids of up to 1536^3 points, and with Taylor Reynolds numbers up to 1100. The initial conditions are either an X-point configuration embedded in 3D, the so-called Orszag-Tang vortex, or an Arn'old-Beltrami-Childress configuration with a fully helical velocity and magnetic field. In each case two snapshots are analyzed, separated by one turn-over time, starting just after the peak of dissipation. We show that the algorithm is able to select a large number of structures (in excess of 8,000) for each snapshot and that the statistical properties of these clusters are remarkably similar for the two snapshots as well as for the two flows under study in terms of scaling laws for the cluster characteristics, with the structures in the vorticity and in the current behaving in the same way. We also study the effect of Reynolds number on cluster statistics, and we finally analyze the properties of these clusters in terms of their velocity-magnetic field correlation. Self-organized criticality features have been identified in the dissipative range of scales. A different scaling arises in the inertial range, which cannot be identified for the moment with a known self-organized criticality class consistent with MHD. We suggest that this range can be governed by turbulence dynamics as opposed to criticality, and propose an interpretation of intermittency in terms of propagation of local instabilities.Comment: 17 pages, 9 figures, 5 table

Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou