方差阴影图中的光渗现象消除算法

方差阴影图算法使用概率的方法计算像素被遮挡的上限概率,通过对深度图滤波的方法来有效地减少阴影图算法中的走样问题,但在深度比较复杂的场景中方差阴影图算法会出现光渗现象,即在应该是阴影的区域却有了亮度.文中使用最小-最大阴影图来辅助消除方差阴影图中的光渗现象,在对深度纹理进行滤波的同时生成一个最小-最大阴影图;在实时绘制场景时,利用最小-最大阴影图来辅助判断当前片元是否完全处在阴影区域内部,由此生成更真实、更准确的阴影.该算法可以很容易地添加到已有的方差阴影图算法的片元处理程序中,并且不会对原有阴影的柔和边界以及绘制的帧率产生影响.

基于阴影图的伪柔和阴影绘制算法

本文提出一种基于阴影图预滤波的伪柔和阴影实时绘制算法。 阴影是真实世界中的一种十分普遍的物理现象，它不但可以反映物体自身的形状，而且可以反映空间中物体与物体之间的相对位置关系，对于增加场景的真实感具有非常重要的意义。因而阴影的实时绘制一直都是计算机图形学中一个重要的研究方向。 方差阴影图算法是对传统阴影图算法的一个重要改进。传统的阴影图算法虽然有着易于实现、硬件支持好、与场景复杂度无关等优点，但是由于它是对场景的离散表示，所以会出现各种各样的走样问题。方差阴影图算法是使用概率的方法计算片元被遮挡的上限概率，它可以通过对深度图滤波的方法来有效地减少阴影图算法中的走样问题。方差阴影图算法克服了传统阴影图算法中边界走样问题，可以生成比较柔和的边界。但是，这种概率方法在深度比较复杂的场景中会出现光渗现象，即在原本应该是阴影的区域却有了亮度。本文使用最小-最大阴影图来辅助消除方差阴影图中的光渗现象。算法在对深度纹理进行滤波的同时生成一个最小-最大阴影图；在从视点实时绘制场景时，利用最小-最大阴影图来辅助判断当前片元是否完全处在阴影区域内部。通过将最小-最大阴影图和方差阴影图相结合，本文的算法可以快速生成伪柔和阴影。

Weighted least-squares phase unwrapping algorithm based on derivative variance correlation map

Among different phase unwrapping approaches, the weighted least-squares minimization methods are gaining attention. In these algorithms, weighting coefficient is generated from a quality map. The intrinsic drawbacks of existing quality maps constrain the application of these algorithms. They often fail to handle wrapped phase data contains error sources, such as phase discontinuities, noise and undersampling. In order to deal with those intractable wrapped phase data, a new weighted least-squares phase unwrapping algorithm based on derivative variance correlation map is proposed. In the algorithm, derivative variance correlation map, a novel quality map, can truly reflect wrapped phase quality, ensuring a more reliable unwrapped result. The definition of the derivative variance correlation map and the principle of the proposed algorithm are present in detail. The performance of the new algorithm has been tested by use of a simulated spherical surface wrapped data and an experimental interferometric synthetic aperture radar (IFSAR) wrapped data. Computer simulation and experimental results have verified that the proposed algorithm can work effectively even when a wrapped phase map contains intractable error sources. (c) 2006 Elsevier GmbH. All rights reserved.

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.

Continuous sky view factor maps from high resolution urban digital elevation models

This paper presents a new method to calculate sky view factors (SVFs) from high resolution urban digital elevation models using a shadow casting algorithm. By utilizing weighted annuli to derive SVF from hemispherical images, the distance light source positions can be predefined and uniformly spread over the whole hemisphere, whereas another method applies a random set of light source positions with a cosine-weighted distribution of sun altitude angles. The 2 methods have similar results based on a large number of SVF images. However, when comparing variations at pixel level between an image generated using the new method presented in this paper with the image from the random method, anisotropic patterns occur. The absolute mean difference between the 2 methods is 0.002 ranging up to 0.040. The maximum difference can be as much as 0.122. Since SVF is a geometrically derived parameter, the anisotropic errors created by the random method must be considered as significant.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

MODIFIED TEST STATISTICS BY INTER-VOXEL VARIANCE SHRINKAGE WITH AN APPLICATION TO fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels in the statistical analysis of fMRI data is desirable in order to improve efficiency. Here we construct a hierarchical model and apply an Empirical Bayes framework on the analysis of group fMRI data, employing techniques used in high throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels, and subsequently to construct an improved test statistic in lieu of the classical t-statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances towards a common value. The shrunken estimator for voxelspecific variance components on the group analyses outperforms the classical residual error estimator in terms of mean squared error. Moreover, the shrunken test-statistic decreases false positive rate when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.

A novel modifier gene for plasma von Willebrand factor level maps to distal mouse chromosome 11

Type 1 von Willebrand disease (VWD), characterized by reduced levels of plasma von Willebrand factor (VWF), is the most common inherited bleeding disorder in humans. Penetrance of VWD is incomplete, and expression of the bleeding phenotype is highly variable. In addition, plasma VWF levels vary widely among normal individuals. To identify genes that influence VWF level, we analyzed a genetic cross between RIIIS/J and CASA/Rk, two strains of mice that exhibit a 20-fold difference in plasma VWF level. DNA samples from F2 progeny demonstrating either extremely high or extremely low plasma VWF levels were pooled and genotyped for 41 markers spanning the autosomal genome. A novel locus accounting for 63% of the total variance in VWF level was mapped to distal mouse chromosome 11, which is distinct from the murine Vwf locus on chromosome 6. We designated this locus Mvwf for “modifier of VWF.” Additional genotyping of as many as 2407 meioses established a high resolution genetic map with gene order Cola1-Itg3a-Ngfr-Mvwf/Gip-Hoxb9-Hoxb1-Cbx·rs2-Cox5a-Gfap. The Mvwf candidate interval between Ngfr and Hoxb9 is ≈0.5 centimorgan (cM). These results demonstrate that a single dominant gene accounts for the low VWF phenotype of RIIIS/J mice in crosses with several other strains. The pattern of inheritance suggests a gain-of-function mutation in a unique component of VWF biosynthesis or processing. Characterization of the human homologue for Mvwf may have relevance for a subset of type 1 VWD cases and may define an important genetic factor modifying penetrance and expression of mutations at the VWF locus.