11 resultados para Single system image

em WestminsterResearch - UK


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Marxian thinking following the TSSI (Temporal Single System Interpretation) of Marx is applied to refute the allegation of a tautology in the resource-based view of the firm--paired with providing an explanation of how and why resources create value--, where resources are synonymous with Marx's categories of constant and variable capital. Refuting the allegation naturally leads to the holy grail of resource-based thinking, i.e. the question of what, conceptually, constitutes a firm's competitive advantage within the industry context. The article achieves its objectives by tying the resource-based view into Marx's theory value.

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Multi-parametric magnetic resonance imaging (mp-MRI) has become an increasingly important method for detecting and treating prostate cancer. Transrectal ultrasound (TRUS) is the most commonly used method for guiding prostate needle biopsy and remains the gold standard for diagnosis of prostate cancer. MRI-to-TRUS image reg- istration is an important technology for enabling computer-assisted targeting of the majority of prostate lesions that are visible in MRI but not independently distinguishable in TRUS images. The aim of this study was to estimate the needle placement accuracy of an image guidance system (SmartTargetÒ), developed by our research group, using a surgical training phantom.

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Rapid developments in display technologies, digital printing, imaging sensors, image processing and image transmission are providing new possibilities for creating and conveying visual content. In an age in which images and video are ubiquitous and where mobile, satellite, and three-dimensional (3-D) imaging have become ordinary experiences, quantification of the performance of modern imaging systems requires appropriate approaches. At the end of the imaging chain, a human observer must decide whether images and video are of a satisfactory visual quality. Hence the measurement and modeling of perceived image quality is of crucial importance, not only in visual arts and commercial applications but also in scientific and entertainment environments. Advances in our understanding of the human visual system offer new possibilities for creating visually superior imaging systems and promise more accurate modeling of image quality. As a result, there is a profusion of new research on imaging performance and perceived quality.

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This essay addresses the issue of the relationship between abstraction and realism that it argues is at stake in the rejection of any primacy accorded to the single image, in favour of a sequencing of photographs according to certain, often novelistic and epic ideas of narrative form. Setting out from the opening text of Allan Sekula’s Fish Story, the article explores the competing tendencies towards what Georg Lukács termed ‘narration’ and ‘description’ as these are traced throughout Sekula's project (in part through a comparison with the contrasting works of Andreas Gursky). The essay concludes by suggesting the ways in which it is the irreducible actuality of abstraction within the concrete everydayness of capitalism's social world that means that all photographic ‘realism’ is intrinsically ‘haunted’ by a certain spectre of that ‘self-moving substance in the ‘shape of money’, as Marx calls it, or of the abstract form of capital itself.

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This paper deals with and details the design of a power-aware adaptive digital image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Power-aware system design at the RTL level without having to redesign arithmetic circuits is used to reduce the power consumption in nomadic devices. Power-aware multipliers with configurable precision are used to trade-off the image-rejection-ratio (IRR) performance with power consumption. Results of the simulation case studies demonstrate that the IRR performance of the power-aware system is comparable to that of the normal implementation albeit degraded slightly, but well within the acceptable limits.

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This paper describes an investigation of changes in image appearance when images are viewed at different image sizes on a high-end LCD device. Two digital image capturing devices of different overall image quality were used for recording identical natural scenes with a variety of pictorial contents. From each capturing device, a total of sixty four captured scenes, including architecture, nature, portraits, still and moving objects and artworks under various illumination conditions and recorded noise level were selected. The test set included some images where camera shake was purposefully introduced. An achromatic version of the image set that contained only lightness information was obtained by processing the captured images in CIELAB space. Rank order experiments were carried out to determine which image attribute(s) were most affected when the displayed image size was altered. These evaluations were carried out for both chromatic and achromatic versions of the stimuli. For the achromatic stimuli, attributes such as contrast, brightness, sharpness and noisiness were rank-ordered by the observers in terms of the degree of change. The same attributes, as well as hue and colourfulness, were investigated for the chromatic versions of the stimuli. Results showed that sharpness and contrast were the two most affected attributes with changes in displayed image size. The ranking of the remaining attributes varied with image content and illumination conditions. Further, experiments were carried out to link original scene content to the attributes that changed mostly with changes in image size.

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The gametocytes of the malaria parasite Plasmodium falciparum are highly resistant to antimalarial drugs. Its presence in the blood can be detected even after a successful malaria treatment. This paper explains a modified Annular Ring Ratio method which successfully locates and differentiates gametocytes of P. falciparum species in thin blood film images. The method can be used as an efficient tool for gametocyte detection for post-treatment malaria diagnosis. It also identifies the presence of any White Blood Cells (WBCs) in the image, and discards other artifacts and non infected cells. It utilizes the information based on structure, color and geometry of the cells and does not require any segmentation or non-illumination correction techniques that are commonly used for cell detection.

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An evaluation of the change in perceived image contrast with changes in displayed image size was carried out. This was achieved using data from four psychophysical investigations, which employed techniques to match the perceived contrast of displayed images of five different sizes. A total of twenty-four S-shape polynomial functions were created and applied to every original test image to produce images with different contrast levels. The objective contrast related to each function was evaluated from the gradient of the mid-section of the curve (gamma). The manipulation technique took into account published gamma differences that produced a just-noticeable-difference (JND) in perceived contrast. The filters were designed to achieve approximately half a JND, whilst keeping the mean image luminance unaltered. The processed images were then used as test series in a contrast matching experiment. Sixty-four natural scenes, with varying scene content acquired under various illumination conditions, were selected from a larger set captured for the purpose. Results showed that the degree of change in contrast between images of different sizes varied with scene content but was not as important as equivalent perceived changes in sharpness.

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What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.

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Assessing the subjective quality of processed images through an objective quality metric is a key issue in multimedia processing and transmission. In some scenarios, it is also important to evaluate the quality of the received images with minimal reference to the transmitted ones. For instance, for closed-loop optimisation of image and video transmission, the quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original images - prior to compression and transmission - are not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original images. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

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Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.