916 resultados para variance shadow maps


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Se proponen novedosas fórmulas para evaluar la certeza de la cartografía

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We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.

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The relationship between pairs of individuals is an important topic in many areas of population and quantitative genetics. It is usually measured as the proportion of thegenome identical by descent shared by the pair and it can be inferred from pedigree information. But there is a variance in actual relationships as a consequence of Mendelian sampling, whose general formula has not been developed. The goal of this work is to develop this general formula for the one-locus situation,. We provide simple expressions for the variances and covariances of all actual relationships in an arbitrary complex pedigree. The proposed method relies on the use of the nine identity coefficients and the generalized relationship coefficients; formulas have been checked by computer simulation. Finally two examples for a short pedigree of dogs and a long pedigree of sheep are given.

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It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

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The type-I intermittency route to (or out of) chaos is investigated within the horizontal visibility (HV) graph theory. For that purpose, we address the trajectories generated by unimodal maps close to an inverse tangent bifurcation and construct their associatedHVgraphs.We showhowthe alternation of laminar episodes and chaotic bursts imprints a fingerprint in the resulting graph structure. Accordingly, we derive a phenomenological theory that predicts quantitative values for several network parameters. In particular, we predict that the characteristic power-law scaling of the mean length of laminar trend sizes is fully inherited by the variance of the graph degree distribution, in good agreement with the numerics. We also report numerical evidence on how the characteristic power-law scaling of the Lyapunov exponent as a function of the distance to the tangent bifurcation is inherited in the graph by an analogous scaling of block entropy functionals defined on the graph. Furthermore, we are able to recast the full set of HV graphs generated by intermittent dynamics into a renormalization-group framework, where the fixed points of its graph-theoretical renormalization-group flow account for the different types of dynamics.We also establish that the nontrivial fixed point of this flow coincides with the tangency condition and that the corresponding invariant graph exhibits extremal entropic properties.

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The aim of this work is to provide the necessary methods to register and fuse the endo-epicardial signal intensity (SI) maps extracted from contrast-enhanced magnetic resonance imaging (ceMRI) with X-ray coronary ngiograms using an intrinsic registrationbased algorithm to help pre-planning and guidance of catheterization procedures. Fusion of angiograms with SI maps was treated as a 2D-3D pose estimation, where each image point is projected to a Plücker line, and the screw representation for rigid motions is minimized using a gradient descent method. The resultant transformation is applied to the SI map that is then projected and fused on each angiogram. The proposed method was tested in clinical datasets from 6 patients with prior myocardial infarction. The registration procedure is optionally combined with an iterative closest point algorithm (ICP) that aligns the ventricular contours segmented from two ventriculograms.

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In this paper we present an adaptive spatio-temporal filter that aims to improve low-cost depth camera accuracy and stability over time. The proposed system is composed by three blocks that are used to build a reliable depth map of static scenes. An adaptive joint-bilateral filter is used to obtain consistent depth maps by jointly considering depth and video information and by adapting its parameters to different levels of estimated noise. Kalman filters are used to reduce the temporal random fluctuations of the measurements. Finally an interpolation algorithm is used to obtain consistent depth maps in the regions where the depth information is not available. Results show that this approach allows to considerably improve the depth maps quality by considering spatio-temporal information and by adapting its parameters to different levels of noise.

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In this paper we present an efficient hole filling strategy that improves the quality of the depth maps obtained with the Microsoft Kinect device. The proposed approach is based on a joint-bilateral filtering framework that includes spatial and temporal information. The missing depth values are obtained applying iteratively a joint-bilateral filter to their neighbor pixels. The filter weights are selected considering three different factors: visual data, depth information and a temporal-consistency map. Video and depth data are combined to improve depth map quality in presence of edges and homogeneous regions. Finally, the temporal-consistency map is generated in order to track the reliability of the depth measurements near the hole regions. The obtained depth values are included iteratively in the filtering process of the successive frames and the accuracy of the hole regions depth values increases while new samples are acquired and filtered

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We propose a new method to automatically refine a facial disparity map obtained with standard cameras and under conventional illumination conditions by using a smart combination of traditional computer vision and 3D graphics techniques. Our system inputs two stereo images acquired with standard (calibrated) cameras and uses dense disparity estimation strategies to obtain a coarse initial disparity map, and SIFT to detect and match several feature points in the subjects face. We then use these points as anchors to modify the disparity in the facial area by building a Delaunay triangulation of their convex hull and interpolating their disparity values inside each triangle. We thus obtain a refined disparity map providing a much more accurate representation of the the subjects facial features. This refined facial disparity map may be easily transformed, through the camera calibration parameters, into a depth map to be used, also automatically, to improve the facial mesh of a 3D avatar to match the subjects real human features.

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In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual representations of the recommender system?s set of items and of their relationships; these graphs show us where the most representative information can be found and which items are rated in a more similar way by the recommender system?s community of users. The visual representations achieved take the shape of phylogenetic trees, displaying the numerical similarity and the reliability between each pair of items considered to be similar. As a case study we provide the results obtained using the public database Movielens 1M, which contains 3900 movies.

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Suslin analytic sets characterize the sets of asymptotic values of entire holomorphic functions. By a theorem of Ahlfors, the set of asymptotic values is finite for a function with finite order of growth. Quasiregular maps are a natural generalization of holomorphic functions to dimensions n ≥ 3 and, in fact, many of the properties of holomorphic functions have counterparts for quasiregular maps. It is shown that analytic sets also characterize the sets of asymptotic values of quasiregular maps in Rn, even for those with finite order of growth. Our construction is based on Drasin's quasiregular sine function

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The purpose of this paper is to analyze the usefulness of traditional indexes, such as NDVI and NDWI along with a recently proposed index (NDDI) using merged data for multiple dates, with the aim of obtaining drought data to facilitate the analysis for government premises. In this study we have used Landsat 7 ETM+ data for the month of June (2001-2009), which merged to get bands with twice the resolution. The three previous indices were calculated from these new bands, getting in turn drought maps that can enhance the effectiveness of decision making.

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Mosaics are high-resolution images obtained aerially and employed in several scientific research areas, such for example, in the field of environmental monitoring and precision agriculture. Although many high resolution maps are obtained by commercial demand, they can also be acquired with commercial aerial vehicles which provide more experimental autonomy and availability. For what regard to mosaicing-based aerial mission planners, there are not so many - if any - free of charge software. Therefore, in this paper is presented a framework designed with open source tools and libraries as an alternative to commercial tools to carry out mosaicing tasks.

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We use residual-delay maps of observational field data for barometric pressure to demonstrate the structure of latitudinal gradients in nonlinearity in the atmosphere. Nonlinearity is weak and largely lacking in tropical and subtropical sites and increases rapidly into the temperate regions where the time series also appear to be much noisier. The degree of nonlinearity closely follows the meridional variation of midlatitude storm track frequency. We extract the specific functional form of this nonlinearity, a V shape in the lagged residuals that appears to be a basic feature of midlatitude synoptic weather systems associated with frontal passages. We present evidence that this form arises from the relative time scales of high-pressure versus low-pressure events. Finally, we show that this nonlinear feature is weaker in a well regarded numerical forecast model (European Centre for Medium-Range Forecasts) because small-scale temporal and spatial variation is smoothed out in the grided inputs. This is significant, in that it allows us to demonstrate how application of statistical corrections based on the residual-delay map may provide marked increases in local forecast accuracy, especially for severe weather systems.

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Multiple-complete-digest mapping is a DNA mapping technique based on complete-restriction-digest fingerprints of a set of clones that provides highly redundant coverage of the mapping target. The maps assembled from these fingerprints order both the clones and the restriction fragments. Maps are coordinated across three enzymes in the examples presented. Starting with yeast artificial chromosome contigs from the 7q31.3 and 7p14 regions of the human genome, we have produced cosmid-based maps spanning more than one million base pairs. Each yeast artificial chromosome is first subcloned into cosmids at a redundancy of ×15–30. Complete-digest fragments are electrophoresed on agarose gels, poststained, and imaged on a fluorescent scanner. Aberrant clones that are not representative of the underlying genome are rejected in the map construction process. Almost every restriction fragment is ordered, allowing selection of minimal tiling paths with clone-to-clone overlaps of only a few thousand base pairs. These maps demonstrate the practicality of applying the experimental and software-based steps in multiple-complete-digest mapping to a target of significant size and complexity. We present evidence that the maps are sufficiently accurate to validate both the clones selected for sequencing and the sequence assemblies obtained once these clones have been sequenced by a “shotgun” method.