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Spatial Filters (spatial + filter)
Selected AbstractsThe design of an optimal filter for monthly GRACE gravity modelsGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 2 2008R. Klees SUMMARY Most applications of the publicly released Gravity Recovery and Climate Experiment monthly gravity field models require the application of a spatial filter to help suppressing noise and other systematic errors present in the data. The most common approach makes use of a simple Gaussian averaging process, which is often combined with a ,destriping' technique in which coefficient correlations within a given degree are removed. As brute force methods, neither of these techniques takes into consideration the statistical information from the gravity solution itself and, while they perform well overall, they can often end up removing more signal than necessary. Other optimal filters have been proposed in the literature; however, none have attempted to make full use of all information available from the monthly solutions. By examining the underlying principles of filter design, a filter has been developed that incorporates the noise and full signal variance,covariance matrix to tailor the filter to the error characteristics of a particular monthly solution. The filter is both anisotropic and non-symmetric, meaning it can accommodate noise of an arbitrary shape, such as the characteristic stripes. The filter minimizes the mean-square error and, in this sense, can be considered as the most optimal filter possible. Through both simulated and real data scenarios, this improved filter will be shown to preserve the highest amount of gravity signal when compared to other standard techniques, while simultaneously minimizing leakage effects and producing smooth solutions in areas of low signal. [source] Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche modelsJOURNAL OF BIOGEOGRAPHY, Issue 12 2009Samuel D. Veloz Abstract Aim, Environmental niche models that utilize presence-only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ,training' and ,testing' purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location, The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods, Two types of niche modelling algorithms , the genetic algorithm for rule-set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) , were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence-only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results, The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions, The findings indicate that care should be taken when interpreting the results from presence-only niche models when training and test data have been randomly partitioned but occurrence data were non-randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions. [source] Dynamic Textures for Image-based Rendering of Fine-Scale 3D Structure and Animation of Non-rigid MotionCOMPUTER GRAPHICS FORUM, Issue 3 2002Dana Cobza The problem of capturing real world scenes and then accurately rendering them is particularly difficult for fine-scale 3D structure. Similarly, it is difficult to capture, model and animate non-rigid motion. We present a method where small image changes are captured as a time varying (dynamic) texture. In particular, a coarse geometry is obtained from a sample set of images using structure from motion. This geometry is then used to subdivide the scene and to extract approximately stabilized texture patches. The residual statistical variability in the texture patches is captured using a PCA basis of spatial filters. The filters coefficients are parameterized in camera pose and object motion. To render new poses and motions, new texture patches are synthesized by modulating the texture basis. The texture is then warped back onto the coarse geometry. We demonstrate how the texture modulation and projective homography-based warps can be achieved in real-time using hardware accelerated OpenGL. Experiments comparing dynamic texture modulation to standard texturing are presented for objects with complex geometry (a flower) and non-rigid motion (human arm motion capturing the non-rigidities in the joints, and creasing of the shirt). Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Image Based Rendering [source] Geographic body size gradients in tropical regions: water deficit and anuran body size in the Brazilian CerradoECOGRAPHY, Issue 4 2009Miguel Á. Olalla-Tárraga A recent interspecific study found Bergmann's size clines for Holarctic anurans and proposed an explanation based on heat balance to account for the pattern. However, this analysis was limited to cold temperate regions, and exploring the patterns in warmer tropical climates may reveal other factors that also influence anuran body size variation. We address this using a Cerrado anuran database. We examine the relationship between mean body size in a grid of 1° cells and environmental predictors and test the relative support for four hypotheses using an AIC-based model selection approach. Also, we considered three different amphibian phylogenies to partition the phylogenetic and specific components of the interspecific variation in body size using a method analogous to phylogenetic eigen vector regression (PVR). To consider the potential effects of spatial autocorrelation we use eigenvector-based spatial filters. We found the largest species inhabiting high water deficit areas in the northeast and the smallest in the wet southwest. Our results are consistent with the water availability hypothesis which, coupled with previous findings, suggests that the major determinant of interspecific body size variation in anurans switches from energy to water towards the equator. We propose that anuran body size gradients reflect effects of reduced surface to volume ratios in larger species to control both heat and water balance. [source] Functional source separation applied to induced visual gamma activityHUMAN BRAIN MAPPING, Issue 2 2008Giulia Barbati Abstract Objective of this work was to explore the performance of a recently introduced source extraction method, FSS (Functional Source Separation), in recovering induced oscillatory change responses from extra-cephalic magnetoencephalographic (MEG) signals. Unlike algorithms used to solve the inverse problem, FSS does not make any assumption about the underlying biophysical source model; instead, it makes use of task-related features (functional constraints) to estimate source/s of interest. FSS was compared with blind source separation (BSS) approaches such as Principal and Independent Component Analysis, PCA and ICA, which are not subject to any explicit forward solution or functional constraint, but require source uncorrelatedness (PCA), or independence (ICA). A visual MEG experiment with signals recorded from six subjects viewing a set of static horizontal black/white square-wave grating patterns at different spatial frequencies was analyzed. The beamforming technique Synthetic Aperture Magnetometry (SAM) was applied to localize task-related sources; obtained spatial filters were used to automatically select BSS and FSS components in the spatial area of interest. Source spectral properties were investigated by using Morlet-wavelet time-frequency representations and significant task-induced changes were evaluated by means of a resampling technique; the resulting spectral behaviours in the gamma frequency band of interest (20,70 Hz), as well as the spatial frequency-dependent gamma reactivity, were quantified and compared among methods. Among the tested approaches, only FSS was able to estimate the expected sustained gamma activity enhancement in primary visual cortex, throughout the whole duration of the stimulus presentation for all subjects, and to obtain sources comparable to invasively recorded data. Hum Brain Mapp 29:131,141, 2008. © 2007 Wiley-Liss, Inc. [source] Far-field characterization of small aperturesJOURNAL OF MICROSCOPY, Issue 2 2003N. I. Petrov Summary A far-field method for characterizing apertures based on the relationship between the relative intensity of propagating modes in a given medium and a small aperture illuminated with a light beam is proposed. A simple optical set-up based on computer-generated holograms and spatial filters is suggested to measure the relative strength of modes and provide axial intensity measurements in the far-field zone. It is shown that the minimal size of a spot that may be measured decreases with an increase in the refractive index of a medium into which light propagates and with the use of high-order spatial mode filters. The intensities transmitted through tapered optical fibre tips have been measured and their aperture diameters determined using window-type spatial filters. The results have been compared with measurements using scanning electron microscopy. [source] | ||||||||||