22 resultados para Image processing, computer-assisted

em University of Queensland eSpace - Australia


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In the present study, the authors sought to determine whether the efficiency and cost-effectiveness of cognitive-behavioral treatment (CBT) for panic disorder could be improved by adjunctive computer-assisted therapy. Eighteen participants who met Diagnostic and Statistical Manual of Mental Disorders (3rd ed., revised; American Psychiatric Association, 1987) criteria for panic disorder were randomly assigned to a 12-session CBT (CBT12) condition (D. H. Barlow & M. G. Craske, 1989) or to a 4-session computer-assisted CBT (CBT4-CA) condition. Palmtop computers, with a program developed to incorporate basic principles of CBT, were used by CBT4-CA clients whenever they felt anxious or wanted to practice the therapy techniques and were used by all participants as a momentary assessment tool. CBT4-CA clients carried the computer at all times and continued to use it for 8 weeks after termination of therapy. Analyses of clinically significant change showed superiority of CBT12 at posttest on some measures; however, there were no differences at follow-up.

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Axial X-ray Computed tomography (CT) scanning provides a convenient means of recording the three-dimensional form of soil structure. The technique has been used for nearly two decades, but initial development has concentrated on qualitative description of images. More recently, increasing effort has been put into quantifying the geometry and topology of macropores likely to contribute to preferential now in soils. Here we describe a novel technique for tracing connected macropores in the CT scans. After object extraction, three-dimensional mathematical morphological filters are applied to quantify the reconstructed structure. These filters consist of sequences of so-called erosions and/or dilations of a 32-face structuring element to describe object distances and volumes of influence. The tracing and quantification methodologies were tested on a set of undisturbed soil cores collected in a Swiss pre-alpine meadow, where a new earthworm species (Aporrectodea nocturna) was accidentally introduced. Given the reduced number of samples analysed in this study, the results presented only illustrate the potential of the method to reconstruct and quantify macropores. Our results suggest that the introduction of the new species induced very limited chance to the soil structured for example, no difference in total macropore length or mean diameter was observed. However. in the zone colonised by, the new species. individual macropores tended to have a longer average length. be more vertical and be further apart at some depth. Overall, the approach proved well suited to the analysis of the three-dimensional architecture of macropores. It provides a framework for the analysis of complex structures, which are less satisfactorily observed and described using 2D imaging. (C) 2002 Elsevier Science B.V. All rights reserved.

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An efficient representation method for arbitrarily shaped image segments is proposed. This method includes a smart way to select wavelet basis to approximate the given image segment, with improved image quality and reduced computational load.

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Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

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Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. In this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications.

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The demand for more pixels is beginning to be met as manufacturers increase the native resolution of projector chips. Tiling several projectors still offers a solution to augment the pixel capacity of a display. However, problems of color and illumination uniformity across projectors need to be addressed as well as the computer software required to drive such devices. We present the results obtained on a desktop-size tiled projector array of three D-ILA projectors sharing a common illumination source. A short throw lens (0.8:1) on each projector yields a 21-in. diagonal for each image tile; the composite image on a 3×1 array is 3840×1024 pixels with a resolution of about 80 dpi. The system preserves desktop resolution, is compact, and can fit in a normal room or laboratory. The projectors are mounted on precision six-axis positioners, which allow pixel level alignment. A fiber optic beamsplitting system and a single set of red, green, and blue dichroic filters are the key to color and illumination uniformity. The D-ILA chips inside each projector can be adjusted separately to set or change characteristics such as contrast, brightness, or gamma curves. The projectors were then matched carefully: photometric variations were corrected, leading to a seamless image. Photometric measurements were performed to characterize the display and are reported here. This system is driven by a small PC cluster fitted with graphics cards and running Linux. It can be scaled to accommodate an array of 2×3 or 3×3 projectors, thus increasing the number of pixels of the final image. Finally, we present current uses of the display in fields such as astrophysics and archaeology (remote sensing).

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One of the challenges in scientific visualization is to generate software libraries suitable for the large-scale data emerging from tera-scale simulations and instruments. We describe the efforts currently under way at SDSC and NPACI to address these challenges. The scope of the SDSC project spans data handling, graphics, visualization, and scientific application domains. Components of the research focus on the following areas: intelligent data storage, layout and handling, using an associated “Floor-Plan” (meta data); performance optimization on parallel architectures; extension of SDSC’s scalable, parallel, direct volume renderer to allow perspective viewing; and interactive rendering of fractional images (“imagelets”), which facilitates the examination of large datasets. These concepts are coordinated within a data-visualization pipeline, which operates on component data blocks sized to fit within the available computing resources. A key feature of the scheme is that the meta data, which tag the data blocks, can be propagated and applied consistently. This is possible at the disk level, in distributing the computations across parallel processors; in “imagelet” composition; and in feature tagging. The work reflects the emerging challenges and opportunities presented by the ongoing progress in high-performance computing (HPC) and the deployment of the data, computational, and visualization Grids.

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Direct and simultaneous observation of root growth and plant water uptake is difficult because soils are opaque. X-ray imaging techniques such as projection radiography or Computer Tomography (CT) offer a partial alternative to such limitations. Nevertheless, there is a trade-off between resolution, large field-of-view and 3-dimensionality: With the current state of the technology, it is possible to have any two. In this study, we used X-ray transmission through thin-slab systems to monitor transient saturation fields that develop around roots as plants grow. Although restricted to 2-dimensions, this approach offers a large field-of-view together with high spatial and dynamic resolutions. To illustrate the potential of this technology, we grew peas in 1 cm thick containers filled with soil and imaged them at regular intervals. The dynamics of both the root growth and the water content field that developed around the roots could be conveniently monitored. Compared to other techniques such as X-ray CT, our system is relatively inexpensive and easy to implement. It can potentially be applied to study many agronomic problems, such as issues related to the impact of soil constraints (physical, chemical or biological) on root development.