Perceived density of road maps

Maps should be designed so that users can comprehend and use the information. Display decisions, such as choosing the scale at which an area is shown, depend on properties of the displayed information such as the perceived density (PD) of the information. Taking a psychophysical approach we suggest that the PD of information in a road map is related to the scale and properties of the mapped area. 54 participants rated the PD of 60 maps from different regions. We provide a simple model that predicts the PD of electronic road map displays, using the logarithm of the number of roads, the logarithm of the number of junctions and the length of the shown roads. The PD model was cross-validated using a different set of 60 maps (n = 44). The model can be used for automatically adjusting display scales and for evaluating map designs, considering the required PD to perform a map-related task.     

Asymptotic level density in topological feature maps

The Kohonen algorithm entails a topology conserving mapping of an input pattern space X subsetR(n) characterized by an a priori probability distribution P(x), xinX, onto a discrete lattice of neurons r with virtual positions w(r)inX. Extending results obtained by Ritter (1991) the authors show in the one-dimensional case for an arbitrary monotonously decreasing neighborhood function h(|r-r'|) that the point density D(W(r)) of the virtual net is a polynomial function of the probability density P(x) with D(w(r))~P(alpha)(w(r)). Here the distortion exponent is given by alpha=(1+12R)/3(1+6R) and is determined by the normalized second moment R of the neighborhood function. A Gaussian neighborhood interaction is discussed and the analytical results are checked by means of computer simulations.     

Learning of fuzzy cognitive maps using density estimate

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods.     

Mesh Simplification

Mesh simplification is an important stage after surface reconstruction since the models produced can contain a large number of polygons making them difficult to manipulate. In this paper we present a mesh simplification algorithm to reduce the number of vertices in a dense mesh of triangles. The algorithm is based on edge operations that are performed in the inside of independent clusters distributed over the entire mesh. The clusters are well-characterized regions that can successfully accept simplification operations. The simplification operations produce only local transformations on the mesh. This region-based, distributed approach permits to easily track and control the changes in the triangulation and avoids the appearance of particular cases that would require a special handling. The algorithm uses two user-specified parameters to guide the operations. These parameters allow various simplification strategies that are illustrated on several dense triangulations.     

Scale detection via keypoint density maps in regular or near-regular textures

In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture “scale” as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A “mode” vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging.     

Technical note A new optimizing technique for preparing lineament density maps

In the present study, a new type of fractal plot had been developed based on the fractal nature of lineaments. This plot displays the effect of varying the counting cell dimension on two counted aspects, which are the total frequency of segmented lineaments and the total lineament to cell intersections. The two candidate lines on the plot intersect at a point which defines the optimal cell dimension (OCD) necessary for preparing an optimized lineament density map. The striking relationship observed between the mean lineament length (MLL) and the OCD is fitted to a linear model for calculating the OCD in terms of the MLL with no further need for any fractal plot. Lineaments from different models and live data both confirmed these new findings. Thus, the behaviour of live lineaments had been properly simulated.     

Generalized View-Dependent Simplification

We propose a technique for performing view-dependent geometry and topology simplifications for level-of-detail-based renderings of large models. The algorithm proceeds by preprocessing the input dataset into a binary tree, the view-dependence tree of general vertex-pair collapses. A subset of the Delaunay edges is used to limit the number of vertex pairs considered for topology simplification. Dependencies to avoid mesh foldovers in manifold regions of the input object are stored in the view-dependence tree in an implicit fashion. We have observed that this not only reduces the space requirements by a factor of two, it also highly localizes the memory accesses at run time. The view-dependence tree is used at run time to generate the triangles for display. We also propose a cubic-spline-based distance metric that can be used to unify the geometry and topology simplifications by considering the vertex positions and normals in an integrated manner.     

Simplification of Articulated Meshes

We present a method for simplifying a polygonal character with an associated skeletal deformation such that the simplified character approximates the original shape well when deformed. As input, we require a set of example poses that are representative of the types of deformations the character undergoes and we produce a multi-resolution hierarchy for the simplified character where all simplified vertices also have associated skin weights. We create this hierarchy by minimizing an error metric for a simplified set of vertices and their skin weights, and we show that this quartic error metric can be effectively minimized using alternating quadratic minimization for the vertices and weights separately. To enable efficient GPU accelerated deformations of the simplified character, we also provide a method that guarantees the maximum number of bone weights per simplified vertex is less than a user specified threshold at all levels of the hierarchy.     

保持视觉外观特征的网格简化

针对附有纹理属性的网格模型,提出并实现了一种保持模型基本外观和形状特征的多分辨率网格简化算法.采用半边折叠操作,综合考虑了网格模型半边的几何重要性和纹理属性重要性,将其作为各半边的折叠代价来确定模型中所有边的折叠顺序.预先对网格模型中的边界边和纹理边进行标记,并在简化过程中进行加权处理.实验结果表明,即使在急剧的模型简化后,该方法仍能很好地保持原有模型的视觉外观和形状特征.     

Simplification of mortality case review

With so many various parties vying for access to the medical record after patient dismissal, one of the least appreciated "competitors" is review of the record for quality care concerns, and more specifically, mortality record review. Through use of existing computerized data, a reporting system can easily be developed that will result in increased availability as well as a more timely and effective mortality case review process. The author details how St. Francis Regional Medical Center in Wichita, Ks., developed such a system.     

Simplification of fuzzy switching functions

This paper is concerned with the study of simplification of fuzzy switching functions. A novel algorithm for generating all fuzzy prime implicants is introduced, followed by a new method of simplification of fuzzy switching functions. This algorithm is then reduced to a simple algorithm that produces only those fuzzy prime implicants that are essential. There areonly two other valid techniques for the minimization of fuzzy switching functions in the literature,^(1,2) and those methods are not very suitable for computerized application. Thus, the principal advantages of this technique are the new concept of direct simplification via essential fuzzy prime implicants (without generation of all of the fuzzy prime implicants), and the fact that the algorithm is the first one to be suitable for efficient computer implementation.On leave from the Computer Science Department, New Mexico Institute of Mining and Technology, Socorro, New Mexico.     

External Memory View-Dependent Simplification

In this paper, we propose a novel external-memory algorithm to support view-dependent simplification for datasets much larger than main memory. In the preprocessing phase, we use a new spanned sub-meshes simplification technique to build view-dependence trees I/O-efficiently, which preserves the correct edge collapsing order and thus assures the run-time image quality. We further process the resulting view-dependence trees to build the meta-node trees, which can facilitate the run-time level-of-detail rendering and is kept in disk . During run-time navigation, we keep in main memory only the portions of the meta-node trees that are necessary to render the current level of details, plus some prefetched portions that are likely to be needed in the near future. The prefetching prediction takes advantage of the nature of the run-time traversal of the meta-node trees, and is both simple and accurate. We also employ the implicit dependencies for preventing incorrect foldovers, as well as main-memory buffer management and parallel processes scheme to separate the disk accesses from the navigation operations, all in an integrated manner. The experiments show that our approach scales well with respect to the main memory size available, with encouraging preprocessing and run-time rendering speeds and without sacrificing the image quality.     

基于细节的自适应网格简化

从模型中保留的几何细节出发,提出一种自适应的三角网格简化算法．该算法首先比较顶点与其相邻点之间的欧氏距离是否超过预先设定的简化尺度,删除小于该尺度的相邻点,再对删除造成的空洞作局部三角剖分．此算法的特点是用细节度参数控制简化模型的整体精度,并且简化的尺度可以根据模型表面的细节情况作自适应调整,自动在细节丰富的区域变小而在细节稀疏的区域变大．通过两组应用实例可以看到,文中算法在有效地降低数据量的同时很好地保持了模型的视觉特征．     

保持模型几何特征的简化算法

对于三维模型来说高曲率变化区域、尖角等几何特征是其外部特征的重要体现,因此在简化过程中如何更好地保持这些特征是体现简化算法优劣的重要标准。本文在已有二次误差算法的基础上,综合考虑面片的相邻面的面积与法矢量对该面片附近区域弯曲程度的影响定义了面片的曲率,并根据曲率的大小划分简化过程中各面片的顺序。在简化过程中尽可能地保留对模型几何部特征相关性大的面片,取得了良好的效果。     

保持模型几何特征的简化算法

对于三维模型来说高曲率变化区域、尖角等几何特征是其外部特征的重要体现,因此在简化过程中如何更好地保持这些特征是体现简化算法优劣的重要标准.本文在已有二次误差算法的基础上,综合考虑面片的相邻面的面积与法矢量对该面片附近区域弯曲程度的影响定义了面片的曲率,并根据曲率的大小划分简化过程中各面片的顺序.在简化过程中尽可能地保留对模型几何部特征相关性大的面片,取得了良好的效果.     

基于核密度估计的三维非等距模型簇的对应关系计算方法

针对三维非等距模型簇的一致对应关系计算问题,提出了一种基于核密度估计的三维非等距模型簇对应关系计算方法.首先引入离散时间演化过程描述符(DEP)提取三维模型表面的特征描述符,得到不同区域的不同分布特征;其次通过核密度估计建立非等距模型间的映射关系;最后利用弹性网罚函数对非等距模型簇映射关系进行凸优化,从而得到更准确的三...     

一种改进决策树剪枝算法的研究

决策树归纳方法的剪枝过程是为了消除最终生成的决策树对训练集的过度适应以及减少结点的数量,但最终生成的决策树依然过于庞大。而有些应用对于决策树的精度要求不是很高。通过对剪枝过程加以优化,使得在牺牲少量精度的同时结点的数量大大减少,从而提高生成规则的可理解性。