Markov Random Field (MRF) algorithms are powerful tools in image analysis to explore contextual information of data. However, the application of these methods to large data means that alternative approaches must be found to circumvent the NP-hard complexity of the MRF optimization. We introduce a MRF-based framework that overcomes this issue by using graph partitioning. The computational complexity is decreased as the optimization/parameter estimation is executed on small subgraphs. PMRF targets 3D microCT datasets, but we include evaluation on the Berkeley Segmentation Dataset (ranking 7th place) to fully compare our method with well-known segmentation algorithms. Segmentation results on the microCT datasets achieve precision higher than 95%.

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (μ-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.

One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. We also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.

In this paper, we present a shape recovery technique in 2D and 3D with specific applications in visualizing and measuring anatomical shapes from medical images. This algorithm models extremely corrugated structures like the brain, is topologically adaptable, is robust, and runs in O(N log N) time where N is the total number of points in the domain. Our two-stage technique is based on the level set shape recovery scheme and the fast marching method for computing solutions to static Hamilton-Jacobi equations.

Level set methods have been used in a variety of settings for problems in computer vision and image processing. A related numerical methodology, known as "fast marching methods", has been developed to solve static Hamilton-Jacobi equations extremely quickly; the techniques rely on conversion to a static problem, and are based on a marriage between narrow band techniques for level set methods and fast sorting algorithms. We show the application of these techniques to a collection of problems, including image denoising and enhancement schemes based on curvature-controlled diffusion with automatic stopping and hierarchical scales, extremely fast shape-from-shading schemes, and shape recovery in medical imaging. Level set methods have also been applied to problems in image denoising and enhancement through curvature-controlled diffusion schemes. An extension of these techniques, known as fast marching methods, has been developed to solve static Hamilton-Jacobi equations which arise in aspects of computer vision. We discuss advances in both of these techniques for such problems.

We present a unified approach to noise removal, image enhancement, and shape recovery in images. The underlying approach relies on the level set formulation of the curve and surface motion, which leads to a class of PDE-based algorithms. Beginning with an image, the first stage of this approach removes noise and enhances the image by evolving the image under flow controlled by min/max curvature and by the mean curvature. This stage is applicable to both salt-and-pepper grey-scale noise and full-image continuous noise present in black and white images, grey-scale images, texture images, and color images. The noise removal/enhancement schemes applied in this stage contain only one enhancement parameter, which in most cases is automatically chosen. The other key advantage of our approach is that a stopping criteria is automatically picked from the image; continued application of the scheme produces no further change. The second stage of our approach is the shape recovery of a desired object; we again exploit the level set approach to evolve an initial curve/surface toward the desired boundary, driven by an image-dependent speed function that automatically stops at the desired boundary.

Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods and overcomes some of their limitations. The authors' techniques can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object's topology is made. A single instance of the authors' model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian (1988) to model propagating solid/liquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a "Hamilton-Jacobi" type equation written for a function in which the interface is a particular level set. A speed term synthesized from the image is used to stop the interface in the vicinity of object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. The authors present a variety of ways of computing the evolving front, including narrow bands, reinitializations, and different stopping criteria. The efficacy of the scheme is demonstrated with numerical experiments on some synthesized images and some low contrast medical images.>

The author reports on work devising massively parallel implementations of vortex methods for computing turbulent flow in highly complex geometries. The challenges involved in fitting evolving compilers, hardware, and I/O to nontraditional fluid mechanics techniques, N-body solvers, and interactive real-time visualization environments are discussed. The results describe algorithms to compute two and three-dimensional inside chambers and around bluff bodies. Some issues involved in developing intuition about parallel implementation of complex codes are examined.

In this paper, we present parallel implementations of two methods for computing turbulent flow in complex geometries.Both methods are based on the random vortex method, which is particularly suited for computing complex, viscous, incompressible flow across a wide range of flow regimes and characteristics.The lirst method is a full vortex method, designed to accurately simulate such fluid phenomenon as vortex shedding, merger, and rollup, as well quantitative features of the flow.The second method, based on a "vortexin-cell" method, is an extremely fast version which can offer qualitative portrayal of the dominant fluid structures and mechanisms useful in the design stage.Both methods are non-standard, containing few of the positive attributes commonly associated with methods that easily lend themselves to massively parallel implementations.They are Lagrangian schemes, in which the position of each computational element is affeeted by all others at each time step.The efficient execution of these methods on a Connection Machine CM-2 requires parallel N-body solvers, parallel elliptic solvers, and pamllel data structures for the adaptive creation of computational elements on the boundary of the confining region.provide timing runs.Both are connected to the Realtime Interactive Visualization Environment described in [10].

A graphics environment for essentially real-time interactive scientific visualization of computational fluid mechanics is described. A researcher can interactively examine fluid data on a frame buffer with animated flow visualization diagnostics that mimic those in the experimental laboratory. This provides an effective and interactive way to analyze the underlying physical mechanisms and to compare results with laboratory experiment. The system is implemented on a data parallel supercomputer attached to a frame buffer. The fluid diagnostic tools include display of moving color contours for scalar fields, smoke/dye injection of passive particles to identify coherent flow structures, and bubble wire tracers for velocity profiles. The system is demonstrated by analyzing data generated from a numerical simulation of laminar and turbulent flow over a backwards-facing step. Input parameters are menu-driven, and images are updated at nine frame updates/s.>