831 resultados para IMAGERY REHEARSAL


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Federal and state policy makers increasingly emphasize the need to reduce highway crash rates. This emphasis is demonstrated in Iowa’s recently released draft Iowa Strategic Highway Safety Plan and by the U.S. Department of Transportation’s placement of “improved transportation safety” at the top of its list of strategic goals. Thus, finding improved methods to enhance highway safety has become a top priority at highway agencies. The objective of this project is to develop tools and procedures by which Iowa engineers can identify potentially hazardous roadway locations and designs, and to demonstrate the utility of these tools by developing candidate lists of high crash locations in the State. An initial task, building an integrated database to facilitate the tools and procedures, is an important product, in and of itself. Accordingly, the Iowa Department of Transportation (Iowa DOT) Geographic Information Management System (GIMS) and Geographic Information System Accident Analysis and Location System (GIS-ALAS) databases were integrated with available digital imagery. (The GIMS database contains roadway characteristics, e.g., lane width, surface and shoulder type, and traffic volume, for all public roadways. GIS-ALAS records include data, e.g., vehicles, drivers, roadway conditions, and the crash severity, for crashes occurring on public roadways during then past 10 years.)

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The following dissertation proposes a qualitative approach on the matter ofadvertising, society and construction of identity, based on the effect of householdappliances commercials in constructing female identity of Spanish women today.Conclusions will be drawn based on a juxtaposition of social background andadvertising content and on how Spanish women of today perceive the evolution offemale imagery depicted in advertisements. The aim is to demonstrate how muchcommercials mirrors society and how far it reinforces paradigms no longer existing

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Remote sensing spatial, spectral, and temporal resolutions of images, acquired over a reasonably sized image extent, result in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is very attractive for monitoring, management, and scienti c activities. With Moore's Law alive and well, more and more parallelism is introduced into all computing platforms, at all levels of integration and programming to achieve higher performance and energy e ciency. Being the geometric calibration process one of the most time consuming processes when using remote sensing images, the aim of this work is to accelerate this process by taking advantage of new computing architectures and technologies, specially focusing in exploiting computation over shared memory multi-threading hardware. A parallel implementation of the most time consuming process in the remote sensing geometric correction has been implemented using OpenMP directives. This work compares the performance of the original serial binary versus the parallelized implementation, using several multi-threaded modern CPU architectures, discussing about the approach to nd the optimum hardware for a cost-e ective execution.

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In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

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Two-dimensional aperture synthesis radiometry is the technologyselected for ESA's SMOS mission to provide high resolution L-bandradiometric imagery. The array topology is a Y-shaped structure. Theposition and number of redundant elements to minimise instrumentdegradation in case of element failure(s) are studied.

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A common problem in video surveys in very shallow waters is the presence of strong light fluctuations, due to sun light refraction. Refracted sunlight casts fast moving patterns, which can significantly degrade the quality of the acquired data. Motivated by the growing need to improve the quality of shallow water imagery, we propose a method to remove sunlight patterns in video sequences. The method exploits the fact that video sequences allow several observations of the same area of the sea floor, over time. It is based on computing the image difference between a given reference frame and the temporal median of a registered set of neighboring images. A key observation is that this difference will have two components with separable spectral content. One is related to the illumination field (lower spatial frequencies) and the other to the registration error (higher frequencies). The illumination field, recovered by lowpass filtering, is used to correct the reference image. In addition to removing the sunflickering patterns, an important advantage of the approach is the ability to preserve the sharpness in corrected image, even in the presence of registration inaccuracies. The effectiveness of the method is illustrated in image sets acquired under strong camera motion containing non-rigid benthic structures. The results testify the good performance and generality of the approach

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Projective homography sits at the heart of many problems in image registration. In addition to many methods for estimating the homography parameters (R.I. Hartley and A. Zisserman, 2000), analytical expressions to assess the accuracy of the transformation parameters have been proposed (A. Criminisi et al., 1999). We show that these expressions provide less accurate bounds than those based on the earlier results of Weng et al. (1989). The discrepancy becomes more critical in applications involving the integration of frame-to-frame homographies and their uncertainties, as in the reconstruction of terrain mosaics and the camera trajectory from flyover imagery. We demonstrate these issues through selected examples

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The 10 June 2000 event was the largest flash flood event that occurred in the Northeast of Spain in the late 20th century, both as regards its meteorological features and its considerable social impact. This paper focuses on analysis of the structures that produced the heavy rainfalls, especially from the point of view of meteorological radar. Due to the fact that this case is a good example of a Mediterranean flash flood event, a final objective of this paper is to undertake a description of the evolution of the rainfall structure that would be sufficiently clear to be understood at an interdisciplinary forum. Then, it could be useful not only to improve conceptual meteorological models, but also for application in downscaling models. The main precipitation structure was a Mesoscale Convective System (MCS) that crossed the region and that developed as a consequence of the merging of two previous squall lines. The paper analyses the main meteorological features that led to the development and triggering of the heavy rainfalls, with special emphasis on the features of this MCS, its life cycle and its dynamic features. To this end, 2-D and 3-D algorithms were applied to the imagery recorded over the complete life cycle of the structures, which lasted approximately 18 h. Mesoscale and synoptic information were also considered. Results show that it was an NS-MCS, quasi-stationary during its stage of maturity as a consequence of the formation of a convective train, the different displacement directions of the 2-D structures and the 3-D structures, including the propagation of new cells, and the slow movement of the convergence line associated with the Mediterranean mesoscale low.

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AbstractThis article demonstrates the importance of Angela Carter's translations of Charles Perrault's contes into English and argues for their profound influence on her subsequent literary career. Against feminist critics who rejected fairy tales as conservative and informed by patriarchal structures and values, Carter reclaimed Perrault for feminism by recovering the critical edge and emancipating potential of his Histoires ou contes du temps passé, Avec des Moralités. This essay shows, through a comparative reading of "La Belle au bois dormant" and "The Sleeping Beauty in the Wood," that Carter opposed the worldly "politics of experience" that she found in Perrault to the Disneyfied imagery of the Sleeping Beauty myth and modernized the critique of early marriages already contained in Perrault's Moralités. The subversive power of Carter's work, therefore, is not directed against Perrault but rather toward cultural and commercial appropriations of the fairy tale, which promote a naïve view of marriage.Recommended CitationHennard Dutheil de la Rochère, Martine. ""But marriage itself is no party": Angela Carter's Translation of Charles Perrault's "La Belle au bois dormant"; or, Pitting the Politics of Experience against the Sleeping Beauty Myth." Marvels & Tales 24.1 (2010).

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Schizotypy refers to a set of personality traits thought to reflect the subclinical expression of the signs and symptoms of schizophrenia. Here, we review the cognitive and brain functional profile associated with high questionnaire scores in schizotypy. We discuss empirical evidence from the domains of perception, attention, memory, imagery and representation, language, and motor control. Perceptual deficits occur early and across various modalities. Whilst the neural mechanisms underlying visual impairments may be linked to magnocellular dysfunction, further effects may be seen downstream in higher cognitive functions. Cognitive deficits are observed in inhibitory control, selective and sustained attention, incidental learning and memory. In concordance with the cognitive nature of many of the aberrations of schizotypy, higher levels of schizotypy are associated with enhanced vividness and better performance on tasks of mental rotation. Language deficits seem most pronounced in higher-level processes. Finally, higher levels of schizotypy are associated with reduced performance on oculomotor tasks, resembling the impairments seen in schizophrenia. Some of these deficits are accompanied by reduced brain activation, akin to the pattern of hypoactivations in schizophrenia spectrum individuals. We conclude that schizotypy is a construct with apparent phenomenological overlap with schizophrenia and stable inter-individual differences that covary with performance on a wide range of perceptual, cognitive and motor tasks known to be impaired in schizophrenia. The importance of these findings lies not only in providing a fine-grained neurocognitive characterisation of a personality constellation known to be associated with real-life impairments, but also in generating hypotheses concerning the aetiology of schizophrenia.

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Introduction: Human experience takes place in the line of mental-time (MT) created through imagination of oneself in different time-points in past or future (self-projection in time). Here we manipulated self-projection in MT not only with respect to one's life-events but also with respect to one's faces from different past and future time-points. Methods: We here compared MTT with respect to one's facial images from different time points in past and future (study 1: MT-faces) as well as with respect to different past and future life events (study 2: MT-events). Participants were asked to make judgments about past and future face images and past and future events from three different time-points: the present (Now), eight years earlier (Past) or eight years later (Future). In addition, as a control task participants were asked to make recognition judgments with respect to faces and memory-related judgments with respect to events without changing their habitual self-location in time. Behavioral measures and functional magnetic resonance imaging (fMRI) activity after subtraction of recognition and memory related activities show both absolute MT and relative MT effects for faces and events, signifying a fundamental brain mechanism of MT, disentangled from episodic memory functions. Results: Behavioural and event-related fMRI activity showed three independent effects characterized by (1) similarity between past recollection and future imagination, (2) facilitation of judgments related to the future as compared to the past, and (3) facilitation of judgments related to time-points distant from the present. These effects were found with respect to faces and events suggesting that the brain mechanisms of MT are independent of whether actual life episodes have to be re-/pre-experienced and recruited a common cerebral network including the medial-temporal, precuneus, inferior-frontal, temporo-parietal, and insular cortices. Conclusions: These behavioural and neural data suggest that self-projection in time is a crucial aspect of MT, relying on neural structures encoding memory, mental imagery, and self. Furthermore our results emphasize the idea that mental temporal processing is more strongly directed to future prediction than to past recollection.

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Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

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Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

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In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.