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The sudden compressive failure of unidirectional (UD) fibre reinforced composites at loads well below their tensile strengths is a cause of practical concern. In this respect and more generally, analytical and numerical models that describe composite behaviour have been hard to verify due to a lack of experimental observation, particularly in 3D. The aim of this paper is to combine fast in-situ X-ray computed tomography (CT) with advanced image analysis to capture the changes in fibre orientation in 3D during uninterrupted progressive loading in compression of a UD glass fibre reinforced polymer (GFRP). By analysing and establishing correspondence between a sequence of time-lapse X-ray CT images of the composite, we are able for the first time to follow each fibre and quantify the progressive deflection that takes place during axial compression in the steps leading up to fibre micro-buckling and kinking. Even at just 25 % of the failure load, fibres have started to tilt in approximately the direction of the ultimate kink band. The rate of tilting increases as the composite approaches the collapse load. More generally, our approach can be applied to investigate the behaviour of a wide range of fibrous materials under changing loading conditions.

Recent developments in multispectral X-ray detectors allow for an efficient identification of materials based on their chemical composition. This has a range of applications including security inspection, which is our motivation. In this paper, we analyze data from a tomographic setup employing the MultiX detector, that records projection data in 128 energy bins covering the range from 20 to 160 keV. Obtaining all information from this data requires reconstructing 128 tomograms, which is computationally expensive. Instead, we propose to reduce the dimensionality of projection data prior to reconstruction and reconstruct from the reduced data. We analyze three linear methods for dimensionality reduction using a dataset with 37 equally-spaced projection angles. Four bottles with different materials are recorded for which we are able to obtain similar discrimination of their content using a very reduced subset of tomograms compared to the 128 tomograms that would otherwise be needed without dimensionality reduction.

A connection between microscopic structure and macroscopic properties is expected for almost all material systems. High-resolution transmission electron microscopy is a technique offering insight into the atomic structure, but the analysis of large image series can be time consuming. The present work describes a method to automatically estimate the atomic structure in two-dimensional materials. As an example graphene is chosen, in which the positions of the carbon atoms are reconstructed. Lattice parameters are extracted in the frequency domain and an initial atom positioning is estimated. Next, a plausible neighborhood structure is estimated. Finally, atom positions are adjusted by simulation of a Markov random field model, integrating image evidence and the strong geometric prior. A pristine sample with high regularity and a sample with an induced hole are analyzed. False discovery rate-controlled large-scale simultaneous hypothesis testing is used as a statistical framework for interpretation of results. The first sample yields, as expected, a homogeneous distribution of carbon–carbon (C–C) bond lengths. The second sample exhibits regions of shorter C–C bond lengths with a preferred orientation, suggesting either strain in the structure or a buckling of the graphene sheet. The precision of the method is demonstrated on simulated model structures and by its application to multiple exposures of the two graphene samples.

Background— Because of the nephrotoxic effects of aminoglycosides, the Danish guidelines on infective endocarditis were changed in January 2007, reducing gentamicin treatment in enterococcal infective endocarditis from 4 to 6 weeks to only 2 weeks. In this pilot study, we compare outcomes in patients with Enterococcus faecalis infective endocarditis treated in the years before and after endorsement of these new recommendations. Methods and Results— A total of 84 consecutive patients admitted with definite left-sided E faecalis endocarditis in the period of 2002 to 2011 were enrolled. Forty-one patients were treated before and 43 patients were treated after January 1, 2007. There were no significant differences in baseline characteristics. At hospitalization, the 2 groups had similar estimated glomerular filtration rates of 66 and 75 mL/min ( P =0.22). Patients treated before January 2007 received gentamicin for a significantly longer period (28 versus 14 days; P P =0.75). At discharge, the patients treated before 2007 had a lower estimated glomerular filtration rate (45 versus 66 mL/min; P =0.008) and a significantly greater decrease in estimated glomerular filtration rate (median, 11 versus 1 mL/min; P =0.009) compared with those treated after 2007. Conclusions— Our present pilot study suggests that the recommended 2-week treatment with gentamicin seems adequate and preferable in treating non–high-level aminoglycoside-resistant E faecalis infective endocarditis. The longer duration of gentamicin treatment is associated with worse renal function. Although the certainty of the clinical outcomes is limited by the sample size, outcomes appear to be no worse with the shorter treatment duration. Randomized, controlled studies are warranted to substantiate these results.

Traditionally, the quality traits of meat products have been estimated by means of physico-chemical methods. Computer vision algorithms on MRI have also been presented as an alternative to these destructive methods since MRI is non-destructive, non-ionizing and innocuous. The use of fractals to analyze MRI could be another possibility for this purpose. In this paper, a new fractal algorithm is developed, to obtain features from MRI based on fractal characteristics. This algorithm is called OPFTA (One Point Fractal Texture Algorithm). Three fractal algorithms were tested in this study: CFA (Classical fractal algorithm), FTA (Fractal texture algorithm) and OPFTA. The results obtained by means of these three fractal algorithms were correlated to the results obtained by means of physico-chemical methods. OPFTA and FTA achieved correlation coefficients higher than 0.75 and CFA reached low relationship for the quality parameters of loins. The best results were achieved for OPFTA as fractal algorithm (0.837 for lipid content, 0.909 for salt content and 0.911 for moisture). These high correlation coefficients confirm the new algorithm as an alternative to the classical computational approaches (texture algorithms) in order to compute the quality parameters of meat products in a non-destructive and efficient way.

Glucagon-like peptide-1 (GLP-1) is a physiological regulator of appetite and long-acting GLP-1 receptor agonists (GLP-1RA) lower food intake and bodyweight in both human and animal studies. The effects are mediated through brain GLP-1Rs, and several brain nuclei expressing the GLP-1R may be involved. To date, mapping the complete location of GLP-1R protein in the brain has been challenged by lack of good antibodies and the discrepancy between mRNA and protein especially relevant in neuronal axonal processes. Here, we present a novel and specific monoclonal GLP-1R antibody for immunohistochemistry with murine tissue and show detailed distribution of GLP-1R expression as well as mapping of GLP-1R mRNA by non-radioactive in situ hybridization. Semi-automated image analysis was performed to map the GLP-1R distribution to atlas plates from the Allen Institute of Brain Science (AIBS). The GLP-1R was abundantly expressed in numerous regions including the septal nucleus, the hypothalamus and the brain stem. GLP-1R protein expression was also observed on neuronal projections in brain regions devoid of any mRNA which has not been observed in earlier reports. Taken together, these findings provide new knowledge on GLP-1R expression in neuronal cell bodies and neuronal projections.

The extra-cardiac work-up in infective endocarditis (IE) comprises a search for primary and secondary infective foci. Whether (18)FDG-PET/CT or WBC-SPECT/CT is superior in detection of clinically relevant extra-cardiac manifestations in IE is unexplored. The objectives of this study were to identify the numbers of positive findings detected by each imaging modality, to evaluate the clinical relevance of these findings and to define the reproducibility for extra-cardiac foci in patients with definite IE. Each modality was evaluated for numbers and location of positive extra-cardiac foci in patients with definite IE. A team of 2 × 2 cardiologists evaluated each finding to determine clinical relevance. Clinical utility was determined by 4 criteria converted into an ordinal scale. Using the manifestation with highest clinical utility rating in each patient, the clinical impact of the two imaging modalities was expressed in a clinical utility score. To evaluate reproducibility for each modality, an imaging core laboratory reviewed all findings. In 55 IE patients, 91 pathological foci were found by FDG-PET/CT and 37 foci were identified by WBC-SPECT/CT (p

Non-invasive food inspection and quality assurance are becoming viable techniques in food production due to the introduction of fast and accessible multispectral X-ray scanners. However, the novel devices produce massive amount of data and there is a need for fast and accurate algorithms for processing it. We apply a sparse classifier for foreign object detection and segmentation in multispectral X-ray. Using sparse methods makes it possible to potentially use fewer variables than traditional methods and thereby reduce acquisition time, data volume and classification speed. We report our results on two datasets with foreign objects, one set with spring rolls and one with minced meat. Our results indicate that it is possible to limit the amount of data stored to 50% of the original size without affecting classification accuracy of materials used for training. The method has attractive computational properties, which allows for fast classification of items in new images.

In this paper, we present, for the first time, the absorption and reduced scattering spectra of commercially available milk and yoghurt products, obtained using photon-time-of-flight spectroscopy. The ability of this technique to separate the contributions from absorption and scattering in the sample provides important information on the chemical composition and micro-structural properties, which are not available with the traditional techniques used in dairy production. The instrument operates in the spectral range from 500 nm to 1030 nm. The reduced scattering coefficient varies from 5 cm−1 for milk with 0.1% fat in the near infrared range, to 60 cm−1 for yoghurt with 3.0% fat in the green wavelength regime. The absorption is within the range of 0.05–0.5cm−1, with only small variation in the absolute value between products. Our results show that the reduced scattering clearly distinguishes milk and yoghurt with the same fat content and can offer a reliable way of monitoring structural formation during milk fermentation.

Active Appearance Models (AAM) are used for annotating or segmenting shapes in biomedical images. Performance relies heavily on the image data used to train the AAM. In this paper we improve the generalization properties of the model by making it robust to slowly varying spatial intensity inhomogeneities which are often seen in Light Sheet Fluorescence Microscopy (LSFM) images. This robustness is achieved by modelling the appearance of an image as a regularized Normalized Gradient Field (rNGF). We perform two experiments to challenge the model. First it is tested using a repeated leave-one-out approach on images with minimal imperfections where the left out images are corrupted by a simulated bias field and segmented using the AAM. Secondly we test the model on LSFM images with common acquisition problems. In both experiments the proposed approach outperforms the often used AAM implementation based on Sum of Squared Differences.

OBJECTIVES: To assess the association between comorbidities and Staphylococcus aureus bacteremia in children aged 5-18 years, thus, in children with a matured immune system. Further, we aimed to identify presumably healthy children acquiring bacteremia.STUDY DESIGN: By cross-linking nationwide registries, we consecutively included all children born from 1995 onward at their 5-year birthday or date of immigration during 2000-2015. We examined incidence rate ratios (IRR) between preselected exposures and microbiologically verified S aureus bacteremia (reference = children without exposure) using Poisson regression models.RESULTS: We followed 1 109 169 children in 2000-2015 during which 307 children (incidence rate: 3.7 per 100 000 person-years) acquired S aureus bacteremia (methicillin-resistant S aureus = 8; 2.6%). Children without known comorbidities or recent contact with the healthcare system comprised 37.1% of infected children. The highest IRRs were observed in children undergoing dialysis or plasmapheresis (IRR = 367.2 [95% CI) = 188.5-715.3]), children with organ transplantation (IRR = 149.5 [95% CI = 73.9-302.2]), and children with cancer (IRR = 102.9 [95% CI = 74.4-142.2]). Positive associations also were observed in children with chromosomal anomalies (IRR = 7.16 [95% CI = 2.96-17.34]), atopic dermatitis (IRR = 4.89 [95% CI = 3.11-7.69]), congenital heart disease (IRR = 3.14 [95% CI = 1.92-5.11]), and in children undergoing surgery (IRR = 3.34 [95% CI = 2.59-4.28]). Neither premature birth nor parental socioeconomic status was associated with increased disease rates.CONCLUSIONS: S aureus bacteremia is uncommon in children between 5 and 18 years of age. Risk factors known from the adult population, such as dialysis, plasmapheresis, organ transplantation, and cancer, were associated with the highest relative rates. However, prematurity and parental socioeconomic status were not associated with increased rates. Approximately one-third of infected children were presumably healthy.

BACKGROUND: The NOVA score is a recently developed diagnostic tool to identify patients with increased risk of infective endocarditis (IE) among patients with Enterococcus faecalis (EF) bacteremia. We aim to validate an adapted version of the NOVA score and to identify risk factors for IE in patients with EF bacteremia.METHODS: From January 1, 2010 to December 31, 2013, we included 647 consecutive patients with EF bacteremia. Predictors of IE were identified with multivariable Cox regression. The NOVA score was used in a slightly adapted form; 2/2 positive blood cultures resulted in 5 points, unknown origin of infection 4 points, prior valve disease 2 points and heart murmur 1 point.RESULTS: IE was diagnosed in 78 patients (12%). Monomicrobial EF bacteremia (HR 3.60; CI95% 1.6-8.0), prosthetic heart valve (HR 6.2; CI95% 3.8-10.1), male sex (HR 2.0; CI95% 1.1-3.8), and community acquisition (HR 1.8; CI95% 1.1-2.9) were independently associated with IE. The adapted NOVA score was applied in the 240 patients examined by echocardiography. A low score (CONCLUSION: Monomicrobial EF bacteremia, community acquisition, prosthetic heart valve and male sex are associated with increased risk of IE. In our retrospective cohort, the adapted NOVA score performed well suggesting it could be useful in guiding clinical decisions.

The quality of a dairy product is largely determined by its microstructure which also affects its optical properties. Consequently, an assessment of the optical properties during production may be part of a feedback system for ensuring the quality of the production process. This paper presents a novel camera-based measurement technique that enables robust quantification of a wide range of reduced scattering coefficients and absorption coefficients. Measurements are based on hyperspectral images of diffuse reflectance in the wavelength range of 470 to 1020 nm. The optical properties of commercially available milk and yogurt products with three different levels of fat content are measured. These constitute a relevant range of products at a dairy plant. The measured reduced scattering properties of the samples are presented and show a clear discrimination between levels of fat contents as well as fermentation. The presented measurement technique and method of analysis is thus suitable for a rapid, non-contact, and non-invasive inspection that can deduce physically interpretable properties.

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists., Comment: 23 pages, 5 figures, accepted for publication in the journal Medical Image Analysis

We present a 54-year-old woman with systemic lupus erythematosus (SLE), fever, pericardial effusion and a mitral valve vegetation. (18)F-Fluorodesoxyglucose positron emission tomography CT ((18)F-FDG-PET-CT) showed very high accumulation of the isotope at the mitral valve. The patient underwent cardiothoracic surgery and pathologic examinations showed characteristic morphology of Libman-Sacks vegetations. All microbiological examinations including blood cultures, microscopy, culture and 16s PCR of the valve were negative and the diagnosis of Libman-Sacks endocarditis was convincing. It is difficult to distinguish Libman-Sacks endocarditis from culture-negative infective endocarditis (IE). Molecular imaging techniques are being used increasingly in cases of suspected IE but no studies have previously reported the use in patients with Libman-Sacks endocarditis. In the present case, (18)F-FDG-PET-CT clearly demonstrated the increased glucose uptake caused by infiltrating white blood cells in the ongoing inflammatory process at the mitral valve. In conclusion, (18)F-FDG-PET-CT cannot be used to distinguish between IE and non-infective Libman-Sacks vegetations.

We describe an efficient algorithm that computes a segmented reconstruction directly from x-ray projection data. Our algorithm uses a parametric curve to define the segmentation. Unlike similar approaches which are based on level-sets, our method avoids a pixel or voxel grid; hence the number of unknowns is reduced to the set of points that define the curve, and attenuation coefficients of the segments. Our current implementation uses a simple closed curve and is capable of separating one object from the background. However, our basic algorithm can be applied to an arbitrary topology and multiple objects corresponding to different attenuation coefficients in the reconstruction. Through systematic tests we demonstrate a high robustness to the noise, and an excellent performance under a small number of projections.

Transparent objects require acquisition modalities that are very different from the ones used for objects with more diffuse reflectance properties. Digitizing a scene where objects must be acquired with different modalities requires scene reassembly after reconstruction of the object surfaces. This reassembly of a scene that was picked apart for scanning seems unexplored. We contribute with a multimodal digitization pipeline for scenes that require this step of reassembly. Our pipeline includes measurement of bidirectional reflectance distribution functions and high dynamic range imaging of the lighting environment. This enables pixelwise comparison of photographs of the real scene with renderings of the digital version of the scene. Such quantitative evaluation is useful for verifying acquired material appearance and reconstructed surface geometry, which is an important aspect of digital content creation. It is also useful for identifying and improving issues in the different steps of the pipeline. In this work, we use it to improve reconstruction, apply analysis by synthesis to estimate optical properties, and to develop our method for scene reassembly.

The color assessment ability of a multispectral vision system is investigated by a comparison study with color measurements from a traditional colorimeter. The experiment involves fresh and processed meat samples. Meat is a complex material; heterogeneous with varying scattering and reflectance properties, so several factors can influence the instrumental assessment of meat color. In order to assess whether two methods are equivalent, the variation due to these factors must be taken into account. A statistical analysis was conducted and showed that on a calibration sheet the two instruments are equally capable of measuring color. Moreover the vision system provides a more color rich assessment of fresh meat samples with a glossier surface, than the colorimeter. Careful studies of the different sources of variation enable an assessment of the order of magnitude of the variability between methods accounting for other sources of variation leading to the conclusion that color assessment using a multispectral vision system is superior to traditional colorimeter assessments.

The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes. Here, we try to take these works a step further by proposing a new multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, we propose a dataset containing 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. To apply this dataset we propose an extension of the evaluation protocol from the Middlebury evaluation, reflecting the more complex geometry of some of our scenes. The proposed dataset is used to evaluate the state of the art multiview stereo algorithms of Tola et al., Campbell et al. and Furukawa et al. Hereby we demonstrate the usability of the dataset as well as gain insight into the workings and challenges of multi-view stereopsis. Through these experiments we empirically validate some of the central hypotheses of multi-view stereopsis, as well as determining and reaffirming some of the central challenges.

We propose an iterative method for detecting closed surfaces in a volumetric data, where an optimal search is performed in a graph build upon a triangular mesh. Our approach is based on previous techniques for detecting an optimal terrain-like or tubular surface employing a regular grid. Unlike similar adaptations for triangle meshes, our method is capable of capturing complex geometries by iteratively refining the surface, where we obtain a high level of robustness by applying explicit mesh processing to intermediate results. Our method uses on-surface data support, but it also exploits data information about the region inside and outside the surface. This provides additional robustness to the algorithm. We demonstrate the capabilities of the approach by detecting surfaces of CT scanned objects.

We report a pattern recognition approach to detect the atomic structure in high-resolution transmission electron microscopy images of graphene. The approach provides quantitative information such as carbon-carbon bond lengths and bond length variations on a global and local scale alike. © 2014 Elsevier Ltd. All rights reserved.

Enterococcus faecalis infective endocarditis (IE) is a disease of increasing importance, with more patients infected, increasing frequency of health-care associated infections and increasing incidence of antimicrobial resistances. The typical clinical presentation is a subacute course with fever, malaise and generalized aches, difficult to distinguish from other more common diseases. Of paramount importance is transthoracic- and transesophageal-echocardiography to establish the diagnosis. At the moment, the predominant strategies recommend ampicillin in combination with either gentamicin or ceftriaxone. E. faecalis infective endocarditis continues to be a very serious disease with considerable percentages of high-level gentamicin resistant strains and in-hospital mortality around 20%. Strategies to prevent E. faecalis IE, improve diagnostics, optimize treatment and reduce morbidity will be necessary to improve the overall prognosis.

In this study, we present a new objective method for measuring the eye colour on a continuous scale that allows researchers to associate genetic markers with different shades of eye colour. With the use of the custom designed software Digital Iris Analysis Tool (DIAT), the iris was automatically identified and extracted from high resolution digital images. DIAT was made user friendly with a graphical user interface. The software counted the number of blue and brown pixels in the iris image and calculated a Pixel Index of the Eye (PIE-score) that described the eye colour quantitatively. The PIE-score ranged from -1 to 1 (brown to blue). The software eliminated the need for user based interpretation and qualitative eye colour categories. In 94% (570) of 605 analyzed eye images, the iris region was successfully extracted and a PIE-score was calculated. A very high correlation between the PIE-score and the human perception of eye colour was observed. The correlations between the PIE-scores and the six IrisPlex SNPs (HERC2 rs12913832, OCA2 rs1800407, SLC24A4 rs12896399, TYR rs1393350, SLC45A2 rs16891982 and IRF4 rs12203592) were analyzed in 570 individuals. Significant differences (p

Diffuse reflectance measurements are useful for noninvasive inspection of optical properties such as reduced scattering and absorption coefficients. Spectroscopic analysis of these optical properties can be used for particle sizing. Systems based on optical fiber probes are commonly employed, but their low spatial resolution limits their validity ranges for the coefficients. To cover a wider range of coefficients, we use camera-based spectroscopic oblique incidence reflectometry. We develop a noninvasive technique for acquisition of apparent particle size distributions based on this approach. Our technique is validated using stable oil-in-water emulsions with a wide range of known particle size distributions. We also measure the apparent particle size distributions of complex dairy products. These results show that our tool, in contrast to those based on fiber probes, can deal with a range of optical properties wide enough to track apparent particle size distributions in a typical industrial process.

The extra-cardiac work-up in infective endocarditis (IE) is comprises a search for both primary and secondary infective foci. Whether 18F-FDG PET/CT (PET/CT) or 99mTc-HMPAO white blood cell SPECT/CT (SPECT/CT) is superior in detection of extra-cardiac manifestations in IE is unexplored. The purposes

We present a case of Staphylococcus aureus endocarditis in which a large abscess was formed in only 4 days, despite specific intravenous antibiotics and only few vague signs of disease progression. Our case seems to be the first to show echocardiographic documentation of how quickly an intracardiac abscess can develop despite relevant antibiotics. Clinically, the patient is remarkably unaffected, and thus even small signs of progression should lead to considerations about repeating the diagnostic imaging workup. This case illustrates how aggressive an infection S. aureus endocarditis may be, and therefore it should be treated by an experienced team with easy access to both transthoracic and transesophageal echocardiography.

Digital prototyping has revolutionised the automotive industry by providing designers and engineers with digital models of their products that enable virtual product design, visualisation, and simulation [1]. However, digital prototyping does not exist in the food industry as the colloidal nature of most foods make them much more challenging to visualise and simulate realistically. We present models and methods that take steps toward digital prototyping of milk products and other food colloids. To simulate the dynamics of liquid products that only exist digitally, we use deformable simplicial complexes with an optimisation-based, linear finite element method [2,3]. Visualisation of products that only exist digitally requires a model for predicting the optical properties of the product materials. The optical properties (absorption coefficient, scattering coefficients, and phase function or asymmetry parameter) are the input needed for a Monte Carlo based graphical rendering. We have developed a model for predicting the optical properties of milk as a function of its fat and protein contents [4]. However, the model has only been validated to a limited extent. We suggest that diffuse reflectance measurements can be used for more extensive validation and for gathering data that can be used to extend our current model such that it can also predict how the optical properties develop during fermentation or acidification of milk to yogurt. A well-established way of measuring optical properties is by static light scattering measurements. This, however, is an invasive procedure where a sample must be placed in a relatively small container (like a cuvette) and scanned by a photon detector orbiting the sample. The container must be small enough to ensure that the sample enters the single scattering regime. Diffuse reflectance measurements have the advantage of being noninvasive. However, the analysis becomes more complex as such measurements include multiple scattering effects. To measure optical properties using diffuse reflectance, we capture high dynamic range images of laser at different wavelengths incident on a sample in situ. The wavelength of the laser is easily adjustable as we use an NKT Photonics SuperK laser [5]. This enables us to retrieve spatially and spectrally resolved diffuse reflectance images. We also acquire images with the laser at several angles of incidence to enable oblique-incidence reflectometry. This enables us to use existing techniques [6,7] for retrieving the apparent optical properties of a sample. The validation consists in comparison of measured optical properties with predicted optical properties.One of our goals is to extend our model for digital prototyping of milk products such that it can also predict how the optical properties develop during gelation of milk to yogurt. The influence of the colloidal aggregation on the optical properties is described by the static structure factor. As our method is noninvasive, we can use our setup for monitoring an acidification process over time. The challenge is to investigate whether we can use the resulting diffuse reflectance images to measure the static structure factor or similar optical properties of gels. We can see some correlation between measured diffuse reflectance and the rheology of the gel. This indicates that some quantity similar to the static structure factor is measurable using spatially resolved diffuse reflectance. There are ways of predicting the static structure factor for different types of colloids [8]. Thus if we succeed in measuring a similar quantity, we can extend our model and validate the extension.This work was (in part) financed by the Centre for Imaging Food Quality project which is funded by the Danish Council for Strategic Research (contract no 09-067039) within the Programme Commission on Health, Food and Welfare. This work was also in part financed by the Digital Prototypes project funded by the Danish Council for Technology and Innovation (Resultatkontrakt).

In this paper we provide a method for evaluating interest point detectors independently of image descriptors. This is possible because we have compiled a unique data set enabling us to determine if common interest points are found. The data contains 60 scenes of a wide range of object types, and for each scene we have 119 precisely located camera positions obtained from a camera mounted on an industrial robot arm. The scene surfaces have been scanned using structured light, providing precise 3D ground truth. We have investigated a number of the most popular interest point detectors. This is done in relation to the number of interest points, the recall rate as a function of camera position and light variation, and the sensitivity relative to model parameter change. The overall conclusion is that the Harris corner detector has a very high recall rate, but is sensitive to change in scale. The Hessian corners perform overall well followed by MSER (Maximally Stable Extremal Regions), whereas the FAST corner detector, IBR (Intensity Based Regions) and EBR (Edge Based Regions) performs poorly. Furthermore, the repeatability of the corner detectors is quite unaffected by the parameter setting, and only the number of interest points change. In this paper we provide a method for evaluating interest point detectors independently of image descriptors. This is possible because we have compiled a unique data set enabling us to determine if common interest points are found. The data contains 60 scenes of a wide range of object types, and for each scene we have 119 precisely located camera positions obtained from a camera mounted on an industrial robot arm. The scene surfaces have been scanned using structured light, providing precise 3Dground truth. We have investigated a number of the most popular interest point detectors where we systematically have varied camera position, light and model parameters. The overall conclusion is that the Harris and Hessian corner detectors perform well followed by MSER, whereas the FAST cornerdetector, IBR and EBR performs poorly. Furthermore, only the number of interest points change with changing parameters - not the correct matches.

Protrusions and retrusions typically form on recrystallizing boundaries and thus the boundaries often appear rough. Characterization of the boundary roughness is necessary in order to evaluate the effects of protrusions and retrusions on boundary migration. In the current work, a variable termed area integral invariant is employed to provide quantitative information of individual protrusions/retrusions on boundaries surrounding two selected recrystallizing grains in partly recrystallized copper as well as of the overall roughness of the boundaries.

The protrusions and retrusions observed on the recrystallizing boundaries affect the migration kinetics during recrystallization. Characterization of the boundary roughness is necessary in order to evaluate their effects. This roughness has a structure that can be characterized by fractal analysis, and in this study the so-called "Minkowski sausage" method is adopted. Hereby, two cube-oriented grains in partly recrystallized microstructures are analyzed and quantitative information regarding the dimensions of protrusions/retrusions is obtained.

Graphene, as the forefather of 2D-materials, attracts much attention due to its extraordinary properties like transparency, flexibility and outstanding high conductivity, together with a thickness of only one atom. The properties seem to be dependent on the atomic structure of graphene and therefore characterizations on the atomic level are of interest. High-resolution transmission electron microscopy (HRTEM) is a state-of-the-art method to characterize the atomic structure of materials. Due to the inherently low mass-thickness of graphene, the contrast levels in the recorded images are often challenging to interpret. In order to increase the signal-to-noise ratio of the images two routes can be pursued: 1) the exposure time can be increased; or 2) acquiring series of images and summarize them after alignment. Both methods have the disadvantage of summing images acquired over a certain period of time making it difficult to resolve dynamic processes or unstable structures. Tools that assist to get the maximum of information out of recorded images are therefore greatly appreciated.In order to get the most accurate results out of the structure detection, we have optimized the imaging conditions used for the FEI Titan ETEM with a monochromator and an objective-lens Cs-corrector. To reduce the knock-on damage of the carbon atoms in the graphene structure, the microscope was operated at 80kV. As this strongly increases the influence of the chromatic aberration of the lenses, the energy spread of the electron gun was reduced. Using the monochromator an energy spread of These images serve as a basis for the image analysis. Single-layer graphene with its regular honeycomb lattice is a perfect model structure to apply automated structure detection. By utilizing Fourier analysis the initial perfect hexagonal structure can easily be recognized. The recorded hexagonal tessellation reflects the unperturbed structure in the image. The centers of the C-hexagons are displayed as nodes. To segment the image into “pure” and “impure” regions, like areas with residual amorphous contamination or defects e.g. holes, a sliding window approach is used. The magnitude of the Fourier transformation within a window is compared to that of a perfect hexagonal tessellation. Areas where this relation exceeds a threshold are recognized as “impure” and a mask is created. As a result, the hexagonal tessellation overlays only the “pure” graphene structure in the image.As the real graphene structure is never perfect and undisturbed, at least at a length-scale of several nm, the model structure has to be adjusted to the real structure. At this point, the image quality plays a crucial role. The algorithm assumes that irregularities in the graphene can be explained by a deformation in the xy-plane. To model this, a set of tensor B-splines is employed, which is deformed by matching model grid points with the C-hexagon centers. Dependent on the Cs and defocus-settings during microscopy these centers appear either dark or bright. One ends up with a deformed hexagonal tessellation, which can easily be transformed into a honeycomb lattice with the C-atom positions included. As the microstructure is now available in the model, information like the C-C distance can be visualized as shown in fig. 2.Applying this method, the perfect graphene structure in recorded HRTEM-images can be determined fast and accurate over a wide length scale, and at the same time lattice deformations can be visualized. The method will be refined to facilitate the detection of larger defects like holes and the determination of the edge terminations.

We use the popular active appearance models (AAM) for extracting discriminative features from images of biological objects. The relevant discriminative features are combined principal component (PCA) vectors from the AAM and texture features from cooccurrence matrices. Texture features are extracted by extending the AAM's with a textural warp guided by the AAM shape. Based on this, texture cooccurrence features are calculated. We use the different features for classifying the biological objects to species using standard classifiers, and we show that even though the objects are highly variant, the AAM's are well suited for extracting relevant features, thus obtaining good classification results. Classification is conducted on two real data sets, one containing various vegetables and one containing different species of wood logs.

We have developed an image analysis pipeline1 that can extract individual fibre tracksfrom low contrast X-ray tomograms of unidirectional composites with high fibre volumefraction. Measuring individual fibre tracks opens up the possibility of modelling thisempirical data in a statistical manner. Thus, allowing to analyse the spatial distributionsof the parameters characterising the orientation and curvature of these individual fibres,which can also provide insights on the interactions amongst the individual fibres.Finite element models (FEMs) can be built from the extracted geometry to simulatethe performance of the scanned fibre structure under realistic conditions. Moreover, as-pects of the fibre architecture that inuence the macroscopic behaviour of the compositecan be quantified. Examples are 2D FEMs to predict the transverse stifiness or the quantification of fibre orientations to estimate the compression strength.1 And last butnot least, already developed analytical and numerical models to describe the composite'sbehaviour can be validated against the observed data.

For many applications in augmented reality (AR), the user has a much more enjoyable experience if the AR system is able to properly guide the user’s attention. In this extended abstract, we explain how to create patterns of light that when projected onto an object are perceived as if the object itself is moving. This can be used as a spatial attention cue. We accomplish this with a calibrated projectorcamera setup to synthesize an image from the projector’s point of view. This image is filtered to create local phasechanges that are then projected back onto the object and perceived as motion. Our method will be shown as a live demonstration at the CV4AR/VR workshop at CVPR 2019

X-ray computed tomography (CT) is a powerful tool for characterising materials for its ability to reveal their internal structure in a non-destructive manner. The recent advances in Xray imaging have brought high-resolution X-rays to laboratory sources, making this tool available to a broader public. Additionally, thanks to the developments in ultra-fast X-ray imaging at synchrotron beamlines, it is now possible to capture the very fast structural changes inside materials under realistic working conditions, e.g. in operation or under loading. There is a need for advanced image analysis methods that can exploit the information contained in these 3D and 4D data-sets of high spatial and temporal resolution, which often contain image artefacts and noise. We have developed a method to characterise the geometry of materials reinforced with long fibres [1], such as glass and carbon fibre reinforced polymers. The method is based on segmenting individual fibres and the task is specially challenging when the image is noisy and its resolution is limited, because the fibres are densely packed. A limited spatial resolution might arise from the need of performing fast scans, to capture the sudden micro-structural changes that happen when reaching the composite’s collapse load, and will facilitate scanning large fields of view containing many fibres, necessary to ensure representative characterisations of a material’s micro-structure. Due to the robustness of our method to image quality [2], we have been able to characterise fibre orientations and diameter distributions in complete bundles, relevant for investigating the effect of the design and manufacturing processes on the mechanical properties of the materials. Moreover, we have applied our methodology to study the behaviour of a fibre composite under compressive loading. Following the changes in each individual fibre under progressive loading conditions, and correlating these with the initial structure of the material, can reveal the precursors to the very complex damage mechanisms that affect fibre composites.

Insegt Fibre is a tool for volumetric fibre segmentation, that is appropriate for densely packed fibres. The robustness to image quality of the underlying dictionary-based algorithm has opened up new opportunities for micro-structural characterisation of fibre-reinforced polymers. The recent user-friendly version of the robust fibre-detection algorithm makes image-based characterisation of fibrous materials simpler and more accessible.

Light scattering in turbid media can be related to the microstructure of media. Thus, light scattering can potentially be used for process control of products where the structure is a key component. However process control requires robust and sensitive input data to function properly. In this study we investigate different decomposition methods for extracting light scattering information from images of diffuse reflectance. Both well-established theoretical methods and data driven methods are considered and evaluated based on their robustness and sensitivity to changes in light scattering properties.