34 resultados para Multi-Exposure Plate Images Processing


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Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.

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Gaussian multi-scale representation is a mathematical framework that allows to analyse images at different scales in a consistent manner, and to handle derivatives in a way deeply connected to scale. This paper uses Gaussian multi-scale representation to investigate several aspects of the derivation of atmospheric motion vectors (AMVs) from water vapour imagery. The contribution of different spatial frequencies to the tracking is studied, for a range of tracer sizes, and a number of tracer selection methods are presented and compared, using WV 6.2 images from the geostationary satellite MSG-2.

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In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

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The impact of novel labels on visual processing was investigated across two experiments with infants aged between 9 and 21 months. Infants viewed pairs of images across a series of preferential looking trials. On each trial, one image was novel, and the other image had previously been viewed by the infant. Some infants viewed images in silence; other infants viewed images accompanied by novel labels. The pattern of fixations both across and within trials revealed that infants in the labelling condition took longer to develop a novelty preference than infants in the silent condition. Our findings contrast with prior research by Robinson and Sloutsky (e.g., Robinson & Sloutsky, 2007a; Sloutsky & Robinson, 2008) who found that novel labels did not disrupt visual processing for infants aged over a year. Provided that overall task demands are sufficiently high, it appears that labels can disrupt visual processing for infants during the developmental period of establishing a lexicon. The results suggest that when infants are processing labels and objects, attentional resources are shared across modalities.

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Parasitic infections cause a myriad of responses in their mammalian hosts, on immune as well as on metabolic level. A multiplex panel of cytokines and metabolites derived from four parasite-rodent models, namely, Plasmodium berghei-mouse, Trypanosoma brucei brucei-mouse, Schistosoma mansoni-mouse, and Fasciola hepatica-rat were statistically coanalyzed. 1H NMR spectroscopy and multivariate statistical analysis were used to characterize the urine and plasma metabolite profiles in infected and noninfected animals. Each parasite generated a unique metabolic signature in the host. Plasma cytokine concentrations were obtained using the ‘Meso Scale Discovery’ multi cytokine assay platform. Multivariate data integration methods were subsequently used to elucidate the component of the metabolic signature which is associated with inflammation and to determine specific metabolic correlates with parasite-induced changes in plasma cytokine levels. For example, the relative levels of acetyl glycoproteins extracted from the plasma metabolite profile in the P. berghei-infected mice were statistically correlated with IFN-γ, whereas the same cytokine was anticorrelated with glucose levels. Both the metabolic and the cytokine data showed a similar spatial distribution in principal component analysis scores plots constructed for the combined murine data, with samples from all infected animals clustering according to the parasite species and whereby the protozoan infections (P. berghei and T. b. brucei) grouped separately from the helminth infection (S. mansoni). For S. mansoni, the main infection-responsive cytokines were IL-4 and IL-5, which covaried with lactate, choline, and D-3-hydroxybutyrate. This study demonstrates that the inherently differential immune response to single and multicellular parasites not only manifests in the cytokine expression, but also consequently imprints on the metabolic signature, and calls for in-depth analysis to further explore direct links between immune features and biochemical pathways.

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This study investigates the production and on-line processing of English tense morphemes by sequential bilingual (L2) Turkish-speaking children with more than three years of exposure to English. Thirty nine 6-9-year-old L2 children and 28 typically developing age-matched monolingual (L1) children were administered the production component for third person –s and past tense of the Test for Early Grammatical Impairment (Rice & Wexler, 1996) and participated in an on-line word-monitoring task involving grammatical and ungrammatical sentences with presence/omission of tense (third person –s, past tense -ed) and non-tense (progressive –ing, possessive ‘s) morphemes. The L2 children’s performance on the on-line task was compared to that of children with Specific Language Impairment (SLI) in Montgomery & Leonard (1998, 2006) to ascertain similarities and differences between the two populations. Results showed that the L2 children were sensitive to the ungrammaticality induced by the omission of tense morphemes, despite variable production. This reinforces the claim about intact underlying syntactic representations in child L2 acquisition despite non target-like production (Haznedar & Schwartz, 1997).

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Dual-system models suggest that English past tense morphology involves two processing routes: rule application for regular verbs and memory retrieval for irregular verbs (Pinker, 1999). In second language (L2) processing research, Ullman (2001a) suggested that both verb types are retrieved from memory, but more recently Clahsen and Felser (2006) and Ullman (2004) argued that past tense rule application can be automatised with experience by L2 learners. To address this controversy, we tested highly proficient Greek-English learners with naturalistic or classroom L2 exposure compared to native English speakers in a self-paced reading task involving past tense forms embedded in plausible sentences. Our results suggest that, irrespective to the type of exposure, proficient L2 learners of extended L2 exposure apply rule-based processing.

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Background: Intrusions are common symptoms of both posttraumatic stress disorder (PTSD) and schizophrenia. Steel et al (2005) suggest that an information processing style characterized by weak trait contextual integration renders psychotic individuals vulnerable to intrusive experiences. This ‘contextual integration hypothesis’ was tested in individuals reporting anomalous experiences in the absence of a need-for-care. Methods: Twenty-six low schizotypes and twenty-three individuals reporting anomalous experiences were shown a traumatic film with and without a concurrent visuo-spatial task. Participants rated post-traumatic intrusions for frequency and form, and completed self-report measures of information processing style. It was predicted that, due to their weaker trait contextual integration, the anomalous experiences group would (1) exhibit more intrusions following exposure to the trauma-film; (2) display intrusions characterised by more PTSD qualities and (3) show a greater reduction of intrusions with the concurrent visuo-spatial task. Results: As predicted, the anomalous experiences group reported a lower level of trait contextual integration and more intrusions than the low schizotypes, both immediately after watching the film, and during the following seven days. Their post-traumatic intrusive memories were more PTSD-like (more intrusive, vivid and associated with emotion). The visuo-spatial task had no effect on number of intrusions in either group. Conclusions: These findings provide some support for the proposal that weak trait contextual integration underlies the development of intrusions within both PTSD and psychosis.

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When performing data fusion, one often measures where targets were and then wishes to deduce where targets currently are. There has been recent research on the processing of such out-of-sequence data. This research has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships among the algorithms so that any approximations made are explicit. Results for a multi-sensor scenario involving out-of-sequence data association are used to illustrate the utility of this approach in a specific context.

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In data fusion systems, one often encounters measurements of past target locations and then wishes to deduce where the targets are currently located. Recent research on the processing of such out-of-sequence data has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships between the algorithms so that any approximations made are explicit.

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Traditional chemometrics techniques are augmented with algorithms tailored specifically for the de-noising and analysis of femtosecond duration pulse datasets. The new algorithms provide additional insights on sample responses to broadband excitation and multi-moded propagation phenomena.

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A theoretical model for predicting the behaviour of membrane distillation by incorporating mass and heat transfer equations has been used to find permeate fluxes, and has been validated experimentally. The model accurately predicts mass and heat transfer. The main work studied the effect of module design using a flat-plate module in laminar flow conditions. Areas of investigation included the use of channels across the membrane surface, decreasing the available membrane surface area, and widening the inlet and outlet channels. The work showed that widening the channels increased the flux. Increased flux was also obtained by the use of channels on the permeate side, though not on the feed side.

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Cereal grains are the dominant source of cadmium in the human diet, with rice being to the fore. Here we explore the effect of geographic, genetic, and processing (milling) factors on rice grain cadmium and rice consumption rates that lead to dietary variance in cadmium intake. From a survey of 12 countries on four continents, cadmium levels in rice grain were the highest in Bangladesh and Sri Lanka, with both these countries also having high per capita rice intakes. For Bangladesh and Sri Lanka, there was high weekly intake of cadmium from rice, leading to intakes deemed unsafe by international and national regulators. While genetic variance, and to a lesser extent milling, provide strategies for reducing cadmium in rice, caution has to be used, as there is environmental regulation as well as genetic regulation of cadmium accumulation within rice grains. For countries that import rice, grain cadmium can be controlled by where that rice is sourced, but for countries with subsistence rice economies that have high levels of cadmium in rice grain, agronomic and breeding strategies are required to lower grain cadmium.

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Resistive respiratory loading is an established stimulus for the induction of experimental dyspnoea. In comparison to unloaded breathing, resistive loaded breathing alters end-tidal CO2 (PETCO2), which has independent physiological effects (e.g. upon cerebral blood flow). We investigated the subjective effects of resistive loaded breathing with stabilized PETCO2 (isocapnia) during manual control of inspired gases on varying baseline levels of mild hypercapnia increased PETCO2). Furthermore, to investigate whether perceptual habituation to dyspnoea stimuli occurs, the study was repeated over four experimental sessions. Isocapnic hypercapnia did not affect dyspnoea unpleasantness during resistive loading. A post hoc analysis revealed a small increase of respiratory unpleasantness during unloaded breathing at +0.6 kPa, the level that reliably induced isocapnia. We didnot observe perceptual habituation over the four sessions. We conclude that isocapnic respiratory loading allows stable induction of respiratory unpleasantness, making it a good stimulus for multi-session studies of dyspnoea.