https://cyberleninka.org/article/n/22310.pdf

Pore-scale imaging and modelling

Course Description This is an advanced course in which we explore the field of Statistical Optics. Topics covered include such subjects as the statistical properties of natural (thermal) and laser light, spatial and temporal coherence, effects of partial coherence on optical imaging instruments, effects on imaging due to randomly inhomogeneous media, and a statistical treatment of the detection of light. Development of this more comprehensive model of the behavior of light draws upon the use of tools traditionally available to the electrical engineer, such as linear system theory and the theory of stochastic processes.

http://ieeexplore.ieee.org/iel5/3/23094/01073023.pdf

Statistical optics

/pdf/high-resolution-x-ray-computed-tomography-in-geosciences-a-3sj7249knj.pdf

High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications

http://research.engr.oregonstate.edu/immiscibles/sites/research.engr.oregonstate.edu.immiscibles/files/Papers/Wildenschild_Sheppard_AWR_2012.pdf

X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems

The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.

/pdf/computer-vision-based-fabric-defect-detection-a-survey-27mr8b2pf3.pdf

Computer-Vision-Based Fabric Defect Detection: A Survey

Abstract The task of detecting flaws in woven textiles can be formulated as the problem of segmenting a “known” non-defective texture from an “unknown” defective texture. In order to discriminate defective texture pixels from non-defective texture pixels, optimal 2-D Gabor filters are designed such that, when applied to non-defective texture, the filter response maximises a Fisher cost function. A pixel of potentially flawed texture is classified as defective or non-defective based on the Gabor filter response at that pixel. The results of this optimised Gabor filter classification scheme are presented for 35 different flawed homogeneous textures. These results exhibit accurate flaw detection with low false alarm rate. Potentially, our novel optimised Gabor filter method could be applied to the more complicated problem of detecting flaws in jacquard textiles. This second and more difficult problem is also discussed, along with some preliminary results.

Optimal Gabor Filters for Textile Flaw Detection

Optimal Gabor filters for textile flaw detection

The understanding of petrophysical and multiphase flow properties is essential for the assessment and exploitation of hydrocarbon reserves; these properties in turn are dependent on the 3D geometric and connectivity properties of the pore space. The determination of the pore size distribution in carbonate rocks remains challenging; extreme variability in carbonate depositional environments and susceptibility to a range of post-depositional processes results in complex pore structures comprising length scales from tens of nanometers to several centimeters. To increase understanding of the role of pore structure on connectivity, conductivity, permeability and recoveries requires one to probe the pore scale structure in carbonates in a continuous range across over seven decades of length scales (from 10 nm to 10 cm) and to integrate information at these different scales. In this paper experimental techniques including micro-computed tomography, backscattered scanning electron microscopy (BSEM), and Focussed ion beam SEM (FIBSEM) are used to probe the pore scale structure in carbonates across many decades of scale. Registration techniques are then used to couple information at different length scales. First an image of a 3D plug (4 cm, 20 micron voxel size) is correlated to a sample at macroporous resolutions (8 mm diameter, 4 micron voxel size). We then focus on coupling SEM and FIBSEM data at submicron resolutions to micro-CT data at ≈3-5 micron resolution. For pixel perfect registration of SEM images, an accurate template has been developed to remove warp artefacts introduced by the SEM scanning procedure and we have successfully mapped the sub-resolution porosity and pore sizes visible in the SEM image to gray scale levels in the 3D image. FIBSEM also allows one to investigate the 3D structure in samples down to tens of nanometers. We briefly discuss how this multiscale information can be used as a method for enhanced analysis of petrophysical properties of carbonates.

Pore Scale Characterization of Carbonates At Multiple Scales: Integration of Micro-CT, BSEM, And FIBSEM

3D X-ray microtomographic imaging and visualization of core material at the pore scale and subsequent analysis of petrophysical properties can give important insight to understanding properties of reservoir core material. 3D images allow one to map in detail the pore and grain structure and interconnectivity of core material. Numerical calculations on image data are in agreement with experimental data for flow and elastic properties on simple core material. This development forms the basis for developing more meaningful structure-property correlations in rock.

Digital rock physics: 3D imaging of core material and correlations to acoustic and flow properties

This paper reports on recent advances at the micro-computed tomography facility at the Australian National University. Since 2000 this facility has been a significant centre for developments in imaging hardware and associated software for image reconstruction, image analysis and image-based modelling. In 2010 a new instrument was constructed that utilises theoretically-exact image reconstruction based on helical scanning trajectories, allowing higher cone angles and thus better utilisation of the available X-ray flux. We discuss the technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60°. We also present dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections, allowing higher speed imaging of time-varying samples. Researchers at the facility have also created a sizeable distributed-memory image analysis toolkit with capabilities ranging from tomographic image reconstruction to 3D shape characterisation. We show results from image registration and present some of the new imaging and experimental techniques that it enables. Finally, we discuss the crucial question of image segmentation and evaluate some recently proposed techniques for automated segmentation.

/pdf/techniques-in-helical-scanning-dynamic-imaging-and-image-2igcxre5vo.pdf

Shane Latham

Papers

Optimal Gabor Filters for Textile Flaw Detection

Optimal Gabor filters for textile flaw detection

Pore Scale Characterization of Carbonates At Multiple Scales: Integration of Micro-CT, BSEM, And FIBSEM

Digital rock physics: 3D imaging of core material and correlations to acoustic and flow properties

Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT