Measurement and rendering of complex non-diffuse and goniochromatic packaging materials

Realistic renderings of materials with complex optical properties, such as goniochromatism and non-diffuse reflection, are difficult to achieve. In the context of the print and packaging industries, accurate visualisation of the complex appearance of such materials is a challenge, both for communication and quality control. In this paper, we characterise the bidirectional reflectance of two homogeneous print samples displaying complex optical properties. We demonstrate that in-plane retro-reflective measurements from a single input photograph, along with genetic algorithm-based BRDF fitting, allow to estimate an optimal set of parameters for reflectance models, to use for rendering. While such a minimal set of measurements enables visually satisfactory renderings of the measured materials, we show that a few additional photographs lead to more accurate results, in particular, for samples with goniochromatic appearance

Predicting visible image degradation by colour image difference formulae

Abstract We carried out a CRT monitor based psychophysical experiment to investigate the quality of three colour image difference metrics, the CIEΔE ab equation, the iCAM and the S-CIELAB metrics. Six original images were reproduced through six gamut mapping algorithms for the observer experiment. The result indicates that the colour image difference calculated by each metric does not directly relate to perceived image difference

GLEEBLE MACHINE DETERMINATION OF CREEP LAW PARAMETERS FOR THERMALLY INDUCED DEFORMATIONS IN ALUMINIUM DC CASTING

By means of a Gleeble machine, the flow stress at steady-state creep in an AA3103 aluminium alloy has been measured for temperatures and strain rates relevant for thermally induced deformations in DC casting. The strain rate has been determined by measuring the global radial strain rate at the specimen center by an extensometer, and the stress has been set equal to the force in the axial direction divided by the cross-section area. The parameters of Garofalo’ s equation have been fitted to the resulting steady-state stress and strain rate. Such a method is based upon the assumption of homogeneous stress and strain rate fields. In the Gleeble machine, the specimens are heated by the Joule effect leading to axial temperature gradients, and the specimen geometry is noncylindrical. The resulting inhomogeneities in the stress and strain rate fields are studied by finite element modeling, and it is shown that although they can be significant, the global radial strain rate and the axial force divided by the cross-section area at the specimen center can be relatively close to what the respective strain rate and stress values would have been if the conditions actually were homogeneous

What causes treatment failure - the patient, primary care, secondary care or inadequate interaction in the health services?

<p>Abstract</p> <p>Background</p> <p>Optimal treatment gives complete relief of symptoms of many disorders. But even if such treatment is available, some patients have persisting complaints. One disorder, from which the patients should achieve complete relief of symptoms with medical or surgical treatment, is gastroesophageal reflux disease (GERD). Despite the fact that such treatment is cheap, safe and easily available; some patients have persistent complaints after contact with the health services. This study evaluates the causes of treatment failure.</p> <p>Methods</p> <p>Twelve patients with GERD and persistent complaints had a semi-structured interview which focused on the patients' evaluation of treatment failure. The interviews were taped, transcribed and evaluated by 18 physicians, (six general practitioners, six gastroenterologists and six gastrointestinal surgeons) who completed a questionnaire for each patient. The questionnaires were scored, and the relative responsibility for the failure was attributed to the patient, primary care, secondary care and interaction in the health services.</p> <p>Results</p> <p>Failing interaction in the health services was the most important cause of treatment failure, followed by failure in primary care, secondary care and the patient himself; the relative responsibilities were 35%, 28%, 27% and 10% respectively. There was satisfactory agreement about the causes between doctors with different specialities, but significant inter-individual differences between the doctors. The causes of the failures differed between the patients.</p> <p>Conclusions</p> <p>Treatment failure is a complex problem. Inadequate interaction in the health services seems to be important. Improved communication between parts of the health services and with the patients are areas of improvement.</p

Individualised Halo-Free Gradient-Domain Colour Image Daltonisation

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known

Variational Anisotropic Gradient-Domain Image Processing

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most often by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and nonlinear local contrast enhancement and colour image Daltonisation illustrate the behaviour of the method

A Computational Framework for Colour Metrics and Colour Space Transforms

An object-oriented computational framework for the transformation of colour data and colour metric tensors is presented. The main idea of the design is to represent the transforms between spaces as compositions of objects from a class hierarchy providing the methods for both the transforms themselves and the corresponding Jacobian matrices. In this way, new colour spaces can be implemented on the fly by transforming from any existing colour space, and colour data in various formats as well as colour metric tensors and colour difference data can easily be transformed between the colour spaces. This reduces what normally requires several days of coding to a few lines of code without introducing a significant computational overhead. The framework is implemented in the Python programming language

Variational Anisotropic Gradient-Domain Image Processing

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most often by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and nonlinear local contrast enhancement and colour image Daltonisation illustrate the behaviour of the method