Gap Closure in (Road) Networks Using Higher-Order Active Contours

We present a new model for the extraction of networks from images in the presence of occlusions. Such occlusions cause gaps in the extracted network that need to he closed. Using higher-order active contours, which allow the incorporation of sophisticated geometric information, we introduce a new, non-local, 'gap closure' force that causes pairs of network extremities that are close together to extend towards one another and join, thus closing the gap between them. We demonstrate the benefits of the model using the problem of road network extraction, presenting results on aerial images

Gap Filling of 3-D Microvascular Networks by Tensor Voting

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated

Phase field models and higher-order active contours

The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a 'phase field' function. The function is not constrained, e.g. to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce 'higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points, in addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images

Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications

Ground motion in areas of abandoned mining: application of the intermittent SBAS (ISBAS) to the Northumberland and Durham coalfield, UK

In this paper, we investigate land motion and groundwater level change phenomena using differential interferometric synthetic aperture radar (DInSAR) over the Northumberland and Durham coalfield in the United Kingdom. The study re-visits earlier research that applied a persistent scatterers interferometry (PSI) technique to ERS (European Remote Sensing) and ENVISAT (Environmental Satellite) data. Here, the Intermittent Small Baseline Subset (ISBAS) DInSAR technique is applied to ERS, ENVISAT and Sentinel-1 SAR datasets covering the late 1990s, the 2000s and the mid-2010s, respectively to increase spatial coverage, aid the geological interpretation and consider the latest Sentinel-1 data. The ERS data identify surface depressions in proximity to former collieries, while all three data sets ascertain broad areas are experiencing regional scale uplift, often occurring in previously mined areas. Uplift is attributed to increases in pore pressure in the overburden following the cessation of groundwater pumping after mine closure. Rising groundwater levels are found to correlate to ground motion measurements at selected monitoring sites, most notably in the surrounding area of Ashington. The area is divided by an impermeable EW fault; to the south, surface heave was identified as groundwater levels rose in the 1990s, whereas to the north, this phenomenon occurred two decades later in the 2010s. The data emphasize the complexity of the post-mining surface and subsurface environment and highlight the benefit that InSAR, utlizing the ISBAS technique, can provide in its charaterization

Neural models of inter-cortical networks in the primate visual system for navigation, attention, path perception, and static and kinetic figure-ground perception

Vision provides the primary means by which many animals distinguish foreground objects from their background and coordinate locomotion through complex environments. The present thesis focuses on mechanisms within the visual system that afford figure-ground segregation and self-motion perception. These processes are modeled as emergent outcomes of dynamical interactions among neural populations in several brain areas. This dissertation specifies and simulates how border-ownership signals emerge in cortex, and how the medial superior temporal area (MSTd) represents path of travel and heading, in the presence of independently moving objects (IMOs). Neurons in visual cortex that signal border-ownership, the perception that a border belongs to a figure and not its background, have been identified but the underlying mechanisms have been unclear. A model is presented that demonstrates that inter-areal interactions across model visual areas V1-V2-V4 afford border-ownership signals similar to those reported in electrophysiology for visual displays containing figures defined by luminance contrast. Competition between model neurons with different receptive field sizes is crucial for reconciling the occlusion of one object by another. The model is extended to determine border-ownership when object borders are kinetically-defined, and to detect the location and size of shapes, despite the curvature of their boundary contours. Navigation in the real world requires humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature. In primates, MSTd has been implicated in heading perception. A model of V1, medial temporal area (MT), and MSTd is developed herein that demonstrates how MSTd neurons can simultaneously encode path curvature and heading. Human judgments of heading are accurate in rigid environments, but are biased in the presence of IMOs. The model presented here explains the bias through recurrent connectivity in MSTd and avoids the use of differential motion detectors which, although used in existing models to discount the motion of an IMO relative to its background, is not biologically plausible. Reported modulation of the MSTd population due to attention is explained through competitive dynamics between subpopulations responding to bottom-up and top- down signals