Two topographic maps for data visualisation

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [Bishop et al. (1997) Neurl Comput 10(1): 215–234]. But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts [Hinton (2000) Technical report GCNU TR 2000-004, Gatsby Computational Neuroscience Unit, University College, London]. We show visualisation results on some real data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data. We compare these mappings on real and artificial data sets. Responsible editor: Soumen Chakrabarti.     

带状大比例尺地形图在分带接边处的接边处理

介绍带状大比例尺地形图在分带处高斯平面上拼接的原理和方法，并编制了相应的接边数据处理程序。作业实践说明，该接边方法简便实用，计算程序正确可靠。     

基于地形学标定的人脸眼睛定位

根据眼睛区域灰度固有特征,从灰度图像地形学标定的角度出发,找到眼睛点的候选集合.然后对平滑后的图像进行数学形态学的膨胀操作,以精简候选集合.最后用投影法对精简后的眼睛对进行验证,从而达到眼睛对精确定位的目的.通过不同质量的人脸数据库进行实验,该方法具有很好的光照鲁棒性,取得了较为满意的定位效果.     

Remote sensing of structural complexity indices for habitat and species distribution modeling

Spatial distribution models are increasingly used in ecological studies, but are limited by the poor accuracy of remote sensing (RS) for mapping microhabitat (< 0.1 ha) features. Mapping accuracy can be improved by combining advanced RS image-processing techniques with microhabitat data expressed as a structural complexity index (SCI). To test this idea, we used principal components analysis (PCA) and an additive SCI method developed for forest ecology (calculated by re-scaling and summing representative structural variables) to summarize 13 microhabitat-scale (0.04 ha) vegetation structure attributes describing the rare mountain bongo antelope's (Tragelaphus eurycerus isaaci) habitat in Kenya's Aberdare mountains. Microhabitat data were collected in 127 plots: 37 related to bongo habitat use, 90 from 1 km-spaced grid points representing overall habitat availability and bongo non-presence. We then assessed each SCI's effectiveness for discerning microhabitat variability and bongo habitat selection, using Wilcoxon Rank Sum tests for differences in mean SCI scores among plots divided into 4 vegetation classes, and the Area Under the Curve (AUC) of receiver operating characteristics from logistic regressions. We also examined the accuracy of predicted SCI scores resulting from regression models based on variables derived from a) ASTER imagery processed with spectral mixture and texture analysis, b) an SRTM DEM and c) rainfall data, using the 90 grid plots for model training and the bongo plots as an independent test dataset. Of the five SCIs derived, two performed best: the PCA-derived Canopy Structure Index (CSI) and an additive index summarizing 8 structural variables (AI₈). CSI and AI₈ showed significant differences between 5 of 6 vegetation class pairs, strong abilities to distinguish bongo-selected from available habitat (AUCs = 0.71 (CSI); 0.70 (AI₈)), and predicted scores 60-110% more accurate than reported by other studies using RS to quantify individual microhabitat structural attributes (CSI model R² = 0.51, RMSE = 0.19 (training) and 0.21 (test); AI₈ model R² = 0.46, RMSE = 0.17 (training) and 0.19 (test)). Repeating the Wilcoxon tests and logistic regressions with RS-predicted SCI values showed that AI₈ most effectively preserved the patterns found with the observed SCIs. These results demonstrate that SCIs effectively characterize microhabitat structure and selection, and boost microhabitat mapping accuracy when combined with enhanced RS image-processing techniques. This approach can improve distribution models and broaden their applicability, makes RS more relevant to applied ecology, and shows that processing field data to be more compatible with RS can improve RS-based habitat mapping accuracy.     

Relational generative topographic mapping

The generative topographic mapping (GTM) has been proposed as a statistical model to represent high-dimensional data by a distribution induced by a sparse lattice of points in a low-dimensional latent space, such that visualization, compression, and data inspection become possible. The formulation in terms of a generative statistical model has the benefit that relevant parameters of the model can be determined automatically based on an expectation maximization scheme. Further, the model offers a large flexibility such as a direct out-of-sample extension and the possibility to obtain different degrees of granularity of the visualization without the need of additional training. Original GTM is restricted to Euclidean data points in a given Euclidean vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows us to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks.     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

基于Snake模型的地图中河流提取算法

地图的手工矢量化是一项耗时、费力的工作。该文根据彩色地图中河流的特点，提出利用改进的Snake模型提取地图中的河流。通过对地图中河流线的结构特征进行统计分析，寻找河流线基元；利用改进主动轮廓模型的自动跟踪功能，实现河流的最终提取。实验结果验证了本算法的有效性。     

应用脑电阻抗地形图检测脑水肿

脑电阻抗的变化与脑水肿、脑梗塞有极大的关系，当脑部发生水肿或者出血时，脑电阻抗值将会发生变化，但足由于数据的有限，空间分辨率低。该文首次提出了采用地形图直观的以图形的方式来显示脑电阻抗数据，提出了将脑电阻抗地形图应用在脑水肿检测的新方法。分析了脑电阻抗地形图所采用的网络拓扑结构，根据拓扑结构信息，以网络中的：角形单元为基础，详细讨论地形图的绘制处理算法。对脑水肿的病人，进行了数据采集，利用计算机形成阻抗地形图，实现r功能性成像。从对病情变化病例的比较分析和稳定病人数据的重复性已经验证了通过这种办法的可行性。     

Estimation of water balance over catchment areas taking into account the heterogeneity of their landscape conditions

The landscape conditions for forming the water balance and runoff from catchment areas can change considerably under the pressure of natural and anthropogenic factors. The role of hydromorphic transformation of landscapes is important for most of the Ob basin; it results in the paludification of the area. The specific parameter n is used in calculating water balance according to Mezentsev’s method of hydro-climatic calculations to take account of landscape conditions. We suggest determining this parameter’s value by geomorphological features of the landscapes using GIS tools.     

府河盘龙大桥主桥施工监控的关键技术

结合府河盘龙大桥主桥施工控制实践，阐述了悬臂浇筑施工过程中的施工监控原理和方法，研究了箱梁的线形预测控制方法和混凝土应变分布特性，以及温度变形对桥梁施工监控的影响，科学地指导了施工。监控结果表明，大桥主桥轴线、合拢段高差、成桥线形、桥面标高均在设计允许误差范围内。