Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun,Korea

The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) satellite image. Landslide hazard areas were analysed and mapped, using the landslide-occurrence factors, by the probability–likelihood ratio method. The results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide locations.     

Detection of landslides using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis

The purpose of this study is to detect landslide locations using web-based digital aerial photographs and to map landslide susceptibility using landslide locations in Jinbu, Korea. The landslide susceptibility map was generated and validated using frequency ratio, weight of evidence, logistic regression and artificial neural network models with a geographic information system (GIS). The landslide locations were identified in the study area from interpretation of digital aerial photographs that were provided on an Internet portal (http://map.daum.net) and checked by field survey. A spatial database of the topography, soil, forest, geology and land use was constructed and landslide-related factors were extracted. Using these factors, landslide susceptibility was analysed using four models. Seventy percent of the landslides were used in landslide susceptibility mapping and the remaining 30% were used for validation. The validation result showed that the frequency ratio, weight of evidence, logistic regression and artificial neural network models had 84.94%, 82.82%, 87.72% and 81.44% accuracies, respectively, representing an overall satisfactory agreement of more than 80%, with the logistic regression model giving the best result. The maps generated could be used to estimate the risk to population, property and existing infrastructure such as the transportation network.     

Real-time landscape model interaction using a tangible geospatial modeling environment

Emerging technologies that combine the flexibility of digital landscape representation with easy-to-interpret 3D physical models open new possibilities for user interaction with geospatial data. A prototype tangible geospatial modeling environment lets users interact with landscape analysis and simulations using a tangible physical model. We introduce a concept that builds upon previous independent tangible user interface (TUI) and terrain analysis research and aims at more intuitive collaborative interaction with digital landscape data.     

Image denoising using statistical model based on quaternion wavelet domain

Image denoising is the basic problem of image processing.Quaternion wavelet transform is a new kind of multiresolution analysis tools.Image via quaternion wavelet transform,wavelet coefficients both in intrascale and in interscale have certain correlations.First,according to the correlation of quaternion wavelet coefficients in interscale,non-Gaussian distribution model is used to model its correlations,and the coefficients are divided into important and unimportance coefficients.Then we use the non-Gaussian distribution model to model the important coefficients and its adjacent coefficients,and utilize the MAP method estimate original image wavelet coefficients from noisy coefficients,so as to achieve the purpose of denoising.Experimental results show that our algorithm outperforms the other classical algorithms in peak signal-to-noise ratio and visual quality.     

基于四元数小波混合统计模型的图像去噪

图像的去噪和压缩一直是图像处理的经典问题,传统的方法中很难将二者 同时兼顾。四元数小波变换是实小波、四元数理论及二维希尔伯特变换相结合的产物,是一 种新的多尺度分析图像处理工具。图像经四元数小波变换后,其小波系数不仅在尺度内具有 相关性,而且在尺度间也具有一定的相关性。文中提出一种混合统计模型,该模型包括尺度 间的二元非高斯分布模型和尺度内的广义高斯分布模型,然后运用最小均方误差（MMSE） 估计从噪声图中的小波系数恢复原图的系数,从而达到去除图像的噪声的目的。仿真实验表 明,论文方法不仅可以获得信噪比上的提高、视觉上达到明显的去噪效果,而且取得了较高 的压缩比。     

Optimization of risk bearing in a statistical model with reinsurance

The paper is devoted to solving problems of optimal choice of client’s risk bearing by the insurer at the level insurer-client and at the level insurer-reinsurer. It was shown that in the model of additional restrictions, the most profitable for the insurer will be the refusal from reinsurance and application of stop-loss insurance strategy. In the problem that takes the restriction on insurer’s risk into account, reinsurance of excess of loss and insurance that is a combination of the stop-loss strategy and deductible are optimal. Necessary and sufficient conditions of optimality of parameters of the stated strategies are obtained; an example illustrating the proved results in case of exponential utility functions is given.     

A piecewise linear approximation based on a statistical model

A statistical model is introduced and then, based on it, a piecewise linear approximation algorithm of linear computational complexity is presented. The advantages of the algorithm are proved experimentally in small sample cases and theoretically in the large sample case. The paper is closed with a discussion on some possible extensions.     

Landslide hazard and bioengineering: towards providing improved decision support through integrated numerical model development

The combined hydrology and stability model (CHASM) is a state-of-the-art software tool that aids the assessment of slope stability. The hydrological component of the model solves the Richard’s equation for saturated and unsaturated groundwater flow to derive the pressure head field as it changes through time. The generated hydrological conditions are used as input to limit equilibrium stability computations to derive factors of safety (FOS). The software can be applied to both site-specific problems as well as wide-area assessment to investigate the impacts of dynamic hydrology, with or without vegetation interactions, on slope stability. The program code, originally developed in a UNIX environment using Fortran ’77, has been implemented in the Microsoft Windows environment using C++ with the addition of a graphical user-interface (GUI) to enable more seamless and user-friendly operation. The GUI allows the user to initialise the slope geometry, define the hydrological, vegetation and geotechnical variables and, impose boundary conditions and initial conditions.     

Object detection based on a robust and accurate statistical multi-point-pair model

In this paper, we propose a robust and accurate background model, called grayscale arranging pairs (GAP). The model is based on the statistical reach feature (SRF), which is defined as a set of statistical pair-wise features. Using the GAP model, moving objects are successfully detected under a variety of complex environmental conditions. The main concept of the proposed method is the use of multiple point pairs that exhibit a stable statistical intensity relationship as a background model. The intensity difference between pixels of the pair is much more stable than the intensity of a single pixel, especially in varying environments. Our proposed method focuses more on the history of global spatial correlations between pixels than on the history of any given pixel or local spatial correlations. Furthermore, we clarify how to reduce the GAP modeling time and present experimental results comparing GAP with existing object detection methods, demonstrating that superior object detection with higher precision and recall rates is achieved by GAP.     

An adaptive-focus deformable model using statistical and geometricinformation

An active contour (snake) model is presented, with emphasis on medical imaging applications. There are three main novelties in the proposed model. First, an attribute vector is used to characterize the geometric structure around each point of the snake model; the deformable model then deforms in a way that seeks regions with similar attribute vectors. This is in contrast to most deformable models which deform to nearby edges without considering geometric structure, and it was motivated by the need to establish point-correspondences that have anatomical meaning. Second, an adaptive-focus statistical model has been suggested which allows the deformation of the active contour in each stage to be influenced primarily by the most reliable matches. Third, a deformation mechanism that is robust to local minima is proposed by evaluating the snake energy function on segments of the snake at a time, instead of individual points. Various experimental results show the effectiveness of the proposed model     

Development and application of a geospatial wildfire exposure and risk calculation tool

Applying wildfire risk assessment models can inform investments in loss mitigation and landscape restoration, and can be used to monitor spatiotemporal trends in risk. Assessing wildfire risk entails the integration of fire modeling outputs, maps of highly valued resources and assets (HVRAs), characterization of fire effects, and articulation of relative importance across HVRAs. Quantifying and geo-processing wildfire risk can be a complex and time-intensive task, often requiring expertise in geospatial analysis. Researchers and land managers alike would benefit from a standardized and streamlined ability to estimate wildfire risk. In this paper we present the development and application of a geospatial wildfire risk calculation tool, FireNVC. We describe the major components of the tool and how they align with a geospatial wildfire risk assessment framework, detail a recent application of the tool to inform federal wildfire management and planning, and offer suggestions for future improvements and uses of the tool.     

Sensitivity testing for improved efficiency of a statistical oil-spill risk analysis model

The US Minerals Management Service performs oil-spill risk analyses using, in part, a statistical model of hypothetical oil-spill trajectories. The “Oil-spill Risk Analysis” (OSRA) model is driven by analyzed sea surface winds and model-generated ocean surface currents. The OSRA model calculates thousands of oil-spill trajectories over extended areas of US waters and tabulates the frequencies with which the simulated oil spills contact the geographic boundaries of designated natural resources within a specified number of days after the simulated spill events are initiated.The limits of computer mass storage and speed impose constraints on the number of oil-spill trajectory simulations and the time step of the numerical integration generating the spill trajectories. Also, the model winds and ocean currents have limited spatial and temporal resolution, producing sensible limits on the spatial density of the simulated oil spills and on the numerical integration time step. This investigation attempted to determine the smallest practical spatial interval between simulated oil spill release sites and the shortest integration time step beyond which the frequencies of oil-spill contact essentially do not change. In addition, we investigated the number of hypothetical oil spills initiated per day from a single location and two methods of numerical integration to generate the spill trajectories—simple forward time stepping and the fourth order Runge–Kutta method.Applying the OSRA model to the Gulf of Mexico using 9 years of winds and ocean current data, we found a smallest distance between simulated spills of 6 nautical miles and a shortest integration time step of 1 h using the fourth order Runge–Kutta integration method. A 20-min time step would be needed with simple forward time stepping. Initiating hypothetical spills eight times per day produced essentially the same contact probabilities as initiating spills once per day. These intervals were practical for OSRA model runs over the US portion of the Gulf of Mexico using commonly available desktop personal computers.     

Recovering facial shape using a statistical model of surface normal direction

In this paper, we show how a statistical model of facial shape can be embedded within a shape-from-shading algorithm. We describe how facial shape can be captured using a statistical model of variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to Cartesian points on a local tangent plane. The distribution of surface normal directions is captured using the covariance matrix for the projected point positions. The eigenvectors of the covariance matrix define the modes of shape-variation in the fields of transformed surface normals. We show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by Lambert's law. We demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details. We assess the accuracy of the technique on a variety of images with ground truth and real-world images     

Child body shape measurement using depth cameras and a statistical body shape model

We present a new method for rapidly measuring child body shapes from noisy, incomplete data captured from low-cost depth cameras. This method fits the data using a statistical body shape model (SBSM) to find a complete avatar in the realistic body shape space. The method also predicts a set of standard anthropometric data for a specific subject without measuring dimensions directly from the fitted model. Since the SBSM was developed using principal component (PC) analysis, we formulate an optimisation problem to fit the model in which the degrees of freedom are defined in PC-score space. The mean unsigned distance between the fitted-model based on depth-camera data and the high-resolution laser scan data was 9.4 mm with a standard deviation (SD) of 5.1 mm. For the torso, the mean distance was 2.9 mm (SD 1.4 mm). The correlations between standard anthropometric dimensions predicted by the SBSM and manually measured dimensions exceeded 0.9.     

Extraction of transliteration pairs from parallel corpora using a statistical transliteration model

This paper describes a framework for modeling the machine transliteration problem. The parameters of the proposed model are automatically acquired through statistical learning from a bilingual proper name list. Unlike previous approaches, the model does not involve the use of either a pronunciation dictionary for converting source words into phonetic symbols or manually assigned phonetic similarity scores between source and target words. We also report how the model is applied to extract proper names and corresponding transliterations from parallel corpora. Experimental results show that the average rates of word and character precision are 93.8% and 97.8%, respectively.     

小邻域统计信息核磁共振医学图像分割模型

目的 提出局部统计信息测地线活动轮廓图像分割方法。方法 该方法采用高斯分布拟合图像局部灰度统计特征信息,构造了方向性驱动项。在此基础上,建立了局部统计信息测地线能量泛函。通过极小化该泛函,来驱动演化曲线有序地向目标边界逼近,最后,整个分割过程采用二值水平集方法实现。结果 本文方法降低了灰度不均匀信息影响,达到提取感兴趣区域轮廓目的,提高算法效率和稳定性。结论 实验结果表明,该方法可以快速准确地分割医学感兴趣目标边界。     

An integrated model based on statistical process control and maintenance

The close relationship between quality and maintenance of manufacturing systems has contributed to the development of integrated models, which use the concept of statistical process control (SPC) and maintenance. Such models not only help to improve quality of products but also lead to lower maintenance cost. In this paper, an integrated model is presented which considers complete failure and planned maintenance simultaneously. This model leads to six different scenarios. A new procedure for calculating average cost per time unit is also presented. Finally, a numerical example is used to evaluate sensitivity of the model parameters and compare performance of the developed model to a planned maintenance model. Results indicate satisfactory performance for the developed model.     

Color juxtaposition for pointillism based on an artistic color model and a statistical analysis

We analyze characteristics and patterns of color juxtaposition and the color theory used by pointillist painters and employ them to create rendering algorithm that generates images in a pointillist style. We determine the distribution of colors in several paintings by Seurat and quantify a number of the theory that he employs. Using an RYB color wheel and a hierarchical point generation technique, we convert an input image into a set of colored dots, which is again converted into brush strokes with appropriate shapes and directions. We present images which illustrate the extent to which we have managed to simulate Seurat’s technique.     

Color object segmentation and tracking using flexible statistical model and level-set

Multimedia Tools and Applications - This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with...     

Analysis of the effects of quantization in multilayer neuralnetworks using a statistical model

A statistical quantization model is used to analyze the effects of quantization when digital techniques are used to implement a real-valued feedforward multilayer neural network. In this process, a parameter called the effective nonlinearity coefficient, which is important in the studying of quantization effects, is introduced. General statistical formulations of the performance degradation of the neural network caused by quantization are developed as functions of the quantization parameters. The formulations predict that the network's performance degradation gets worse when the number of bits is decreased; that a change of the number of hidden units in a layer has no effect on the degradation; that for a constant effective nonlinearity coefficient and number of bits, an increase in the number of layers leads to worse performance degradation; and the number of bits in successive layers can be reduced if the neurons of the lower layer are nonlinear