DarSIA: An Open-Source Python Toolbox for Two-Scale Image Processing of Dynamics in Porous Media

Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where pore-scale grains and interfaces may be clearly visible in the image, yet continuous Darcy-scale parameters may be what are desirable to quantify. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we extend classical image processing concepts to what we define as “physical images” of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials.publishedVersio

The Role of Knowledge in Visual Shape Representation

This report shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important geometrical relationships comprising objects' shapes. Two computational tools are offered: (1) Shapestokens are placed on a Scale-Space Blackboard, (2) Dimensionality-reduction captures deformation classes in configurations of tokens. Knowledge lies in the token types and deformation classes tailored to the constraints and regularities ofparticular shape worlds. A hierarchical shape vocabulary has been implemented supporting several later visual tasks in the two-dimensional shape domain of the dorsal fins of fishes

IST Austria Thesis

This thesis describes a brittle fracture simulation method for visual effects applications. Building upon a symmetric Galerkin boundary element method, we first compute stress intensity factors following the theory of linear elastic fracture mechanics. We then use these stress intensities to simulate the motion of a propagating crack front at a significantly higher resolution than the overall deformation of the breaking object. Allowing for spatial variations of the material's toughness during crack propagation produces visually realistic, highly-detailed fracture surfaces. Furthermore, we introduce approximations for stress intensities and crack opening displacements, resulting in both practical speed-up and theoretically superior runtime complexity compared to previous methods. While we choose a quasi-static approach to fracture mechanics, ignoring dynamic deformations, we also couple our fracture simulation framework to a standard rigid-body dynamics solver, enabling visual effects artists to simulate both large scale motion, as well as fracturing due to collision forces in a combined system. As fractures inside of an object grow, their geometry must be represented both in the coarse boundary element mesh, as well as at the desired fine output resolution. Using a boundary element method, we avoid complicated volumetric meshing operations. Instead we describe a simple set of surface meshing operations that allow us to progressively add cracks to the mesh of an object and still re-use all previously computed entries of the linear boundary element system matrix. On the high resolution level, we opt for an implicit surface representation. We then describe how to capture fracture surfaces during crack propagation, as well as separate the individual fragments resulting from the fracture process, based on this implicit representation. We show results obtained with our method, either solving the full boundary element system in every time step, or alternatively using our fast approximations. These results demonstrate that both of these methods perform well in basic test cases and produce realistic fracture surfaces. Furthermore we show that our fast approximations substantially out-perform the standard approach in more demanding scenarios. Finally, these two methods naturally combine, using the full solution while the problem size is manageably small and switching to the fast approximations later on. The resulting hybrid method gives the user a direct way to choose between speed and accuracy of the simulation

Fine-Grained Image Analysis with Deep Learning: A Survey

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.Comment: Accepted by IEEE TPAM

A machine learning approach to statistical shape models with applications to medical image analysis

Statistical shape models have become an indispensable tool for image analysis. The use of shape models is especially popular in computer vision and medical image analysis, where they were incorporated as a prior into a wide range of different algorithms. In spite of their big success, the study of statistical shape models has not received much attention in recent years. Shape models are often seen as an isolated technique, which merely consists of applying Principal Component Analysis to a set of example data sets. In this thesis we revisit statistical shape models and discuss their construction and applications from the perspective of machine learning and kernel methods. The shapes that belong to an object class are modeled as a Gaussian Process whose parameters are estimated from example data. This formulation puts statistical shape models in a much wider context and makes the powerful inference tools from learning theory applicable to shape modeling. Furthermore, the formulation is continuous and thus helps to avoid discretization issues, which often arise with discrete models. An important step in building statistical shape models is to establish surface correspondence. We discuss an approach which is based on kernel methods. This formulation allows us to integrate the statistical shape model as an additional prior. It thus unifies the methods of registration and shape model fitting. Using Gaussian Process regression we can integrate shape constraints in our model. These constraints can be used to enforce landmark matching in the fitting or correspondence problem. The same technique also leads directly to a new solution for shape reconstruction from partial data. In addition to experiments on synthetic 2D data sets, we show the applicability of our methods on real 3D medical data of the human head. In particular, we build a 3D model of the human skull, and present its applications for the planning of cranio-facial surgeries

Assessing the potential for suffusion in sands using x-ray micro-CT images

Internal erosion is a major safety concern for embankment dams and flood embankments and is the focus of much research internationally. Suffusion is a mechanism of internal erosion which affects gap-graded or broadly graded cohesionless soils and is characterised by selective removal of fine material, leaving behind a coarse material with increased hydraulic conductivity. Early studies on suffusion proposed design criteria based on laboratory testing, and presented conceptual models to explain the results in terms of grain-scale behaviour. The study by Kenney & Lau (1985) identified three criteria for suffusion: 1 – Fine particles must be free to move (mechanical criterion); 2 – Fine particles must be small enough to fit through the void space between coarse particles (geometric criterion); 3 – Fluid flowing through the void space must have sufficient velocity to transport the fine particles (hydraulic criterion). Recent studies have examined the first two criteria using grain-scale models with idealised particles, including analytical models and discrete element models (DEM) with circular or spherical particles. This thesis presents a new methodology, using non-destructive 3D imaging (micro-CT) to characterise the internal microstructure in physical specimens of sands and glass beads. This methodology involved the development of innovative image processing and numerical techniques to quantify unstable particle assemblies and to measure particle size distributions and void constriction size distributions. The new method was validated and was shown to produce good agreement with existing methods for idealised particle configurations, however the results for real sand specimens provided new insights into the effects of particle shape, particle size distribution and density on void constriction sizes. Furthermore, the 3D images of real specimens have provided new insights into the appropriateness of existing conceptual models for gap-graded particle structures. These results were used to critically examine and evaluate existing mechanical and geometric criteria for suffusion. The 3D images showed, qualitatively, that the void structures in sands varied significantly from those in porous rocks – which had been the basis for the majority of existing grain-scale fluid flow models. To examine this issue quantitatively, computational fluid dynamics (CFD) simulations were performed within the 3D images of sands and glass beads, in parallel to laboratory permeameter tests on the same materials. The results presented in this thesis provided entirely new insights into the patterns of fluid flow in sands, they allowed correlations to be made between fluid flow and void constriction sizes and also showed how local velocities varied from volume-average discharge and seepage velocities. This study provides new information to support, clarify and improve upon the current understanding of suffusion, filtration and seepage flows in sands. The detailed methodology and results also highlight issues of great importance to future micro-scale modelling of these phenomena.Open Acces