Edge-Forming Methods for Image Zooming

The article is concerned with edge-forming methods to be applied as a post-process for image zooming. Image zooming via standard interpolation methods often produces the so-called checkerboard effect, in particular, when the magnification factor is large. In order to remove the artifact and to form reliable edges, a nonlinear semi-discrete model and its numerical algorithm are suggested along with anisotropic edge-forming numerical schemes. The algorithm is analyzed for stability and choices of parameters. For image zooming by integer factors, a few iterations of the algorithm can form clear and sharp edges for gray-scale images. Various examples are presented to show effectiveness and efficiency of the newly-suggested edge-forming strategy. The work of this author is supported in part by NSF grant DMS–0312223. Youngjoon Cha received his B.Sc. (1988) and M.Sc. (1990) from Mathematics, Seoul National University, Seoul, South Korea; and Ph.D. (1996) from Mathematics, Purdue University, working on mathematical epidemiology, under a guidance of Prof. Fabio Milner. He was a post-doctoral researcher at Purdue University, and Seoul National University, South Korea, from 1996 to 1997 and from 1997 to 1998, respectively. He is currently an associate professor in the Department of Applied Mathematics, Sejong University, South Korea. His research interests include image processing, mathematical and numerical modeling for waves, and mathematical epidemiology. Seongjai Kim received his B.Sc. (1988) and M.Sc. (1990) from Mathematics, Seoul National University, Seoul, South Korea; and Ph.D. (1995) from Mathematics, Purdue University, working on computational fluid dynamics, under a guidance of Prof. Jim Douglas, Jr. After two years of post-doctoral research on seismic inversion at Rice University, he worked for Shell E&P Tech. Co., Houston, for a year and the Department of Mathematics, University of Kentucky, for seven years. He is currently an associate professor in the Department of Mathematics and Statistics, Mississippi State University. His research interests are in mathematical and numerical modeling for wave propagation in highly heterogeneous media, seismology, and image processing for challenging images.     

具有敏感区域的华南虎图像配准研究

对数极坐标映射是一种具有刚体不变性的模板匹配算法,以它获得的刚体变换矩阵为初值经LMA(Levenberg Marquardt algorithm)迭代可以实现图像的高精度配准,但对于场景发生剧烈变化的图像对,上述方法无法获得正确的配准参数。为此,提出了一种具有敏感区域的图像配准算法,在LMA迭代中仅考虑落在人工选取的敏感区域中的像素点,通过对陕西林业厅公布的华南虎照片进行的图像配准实验表明了该算法的有效性。     

Mapping and monitoring land cover in the Willandra Lakes World Heritage Region

Abstract

Landsat digital data is used to map land cover in the Willandra Lakes World Heritage Region in the semi-arid region of New South Wales. An unsupervised classification using a migrating means clustering algorithm followed by a maximum-likelihood classification procedure was found to be the most discriminating method of classifying land cover. The results of applying three change-detection routines to multitemporal data sets of selected sites within the region are presented and discussed.     

Analysis of thinning algorithms using mathematical morphology

A precise definition of digital skeletons and a mathematical framework for the analysis of a class of thinning algorithms, based on morphological set transformation, are presented. A particular thinning algorithm (algorithm A) is used as an example in the analysis. Precise definitions and analyses associated with the thinning process are presented, including the proof of convergence, the condition for one-pixel-thick skeletons, and the connectedness of skeletons. In addition, a necessary and sufficient condition for the thinning process in general is derived, and an algorithm (algorithm B) based on this condition is developed. Experimental results are used to compare the two thinning algorithms, and issues involving noise immunity and skeletal bias are addressed     

Cloud and precipitation classification for a depression system approaching the south Balkan Peninsula. A case study of 26 March 1998

A method is presented that has been developed for classifying cloud and precipitation for a specific synoptic situation affecting south-east Europe during winter and spring: a depression system that gives rise to a heavy rainfall weather situation. The classification algorithm used for classifying clouds and precipitation is based on a combination of spectral channels and the multispectral box classification technique using visible and infrared Meteosat data. In order to increase the accuracy of the classification algorithm, the appropriate cloud types are selected according to the specific synoptic weather situation. The classification algorithm produces nine classes, four of which involve precipitation. These are: (1) clear or insignificant clouds, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light-moderate precipitation, (8) middle-high cloud with moderate-heavy precipitation, (9) heavy thunderstorm. The classifier has been tested for its accuracy (ground truth) using comparisons between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification produced values. The case study of 26 March 1998 is presented. The classifier used has proved to be very reliable for classifying major cloud types and precipitation during the synoptic situation of depression systems approaching the south Balkan peninsula from the west. It has been tested for different intensities of rainfall as well as for heavy thunderstorms and the results are very satisfactory. The results produced can be used to support the forecaster's daily work. The need for the extraction of such classification products lies in the fact that detailed weather information is demanded for regions in Greece devoid of surface observations, such as the Aegean Sea, the Ionian Sea, the South Cretan Sea and remote mountainous areas. That information has been found to be useful, especially in cases where significant weather systems are approaching Greece from the west or south-west, where surface observations over the sea are not available and an early knowledge of the heaviness of precipitation is needed, even before that weather system is in range of any land-based weather radar. Moreover, the single use of isolated channels (i.e. infrared or visible) could involve the danger of errors in the interpretation of the satellite image.     

Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data

The remote sensing of Earth surface changes is an active research field aimed at the development of methods and data products needed by scientists, resource managers, and policymakers. Fire is a major cause of surface change and occurs in most vegetation zones across the world. The identification and delineation of fire-affected areas, also known as burned areas or fire scars, may be considered a change detection problem. Remote sensing algorithms developed to map fire-affected areas are difficult to implement reliably over large areas because of variations in both the surface state and those imposed by the sensing system. The availability of robustly calibrated, atmospherically corrected, cloud-screened, geolocated data provided by the latest generation of moderate resolution remote sensing systems allows for major advances in satellite mapping of fire-affected area. This paper describes an algorithm developed to map fire-affected areas at a global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series data. The algorithm is developed from the recently published Bi-Directional Reflectance Model-Based Expectation change detection approach and maps at 500 m the location and approximate day of burning. Improvements made to the algorithm for systematic global implementation are presented and the algorithm performance is demonstrated for southern African, Australian, South American, and Boreal fire regimes. The algorithm does not use training data but rather applies a wavelength independent threshold and spectral constraints defined by the noise characteristics of the reflectance data and knowledge of the spectral behavior of burned vegetation and spectrally confusing changes that are not associated with burning. Temporal constraints are applied capitalizing on the spectral persistence of fire-affected areas. Differences between mapped fire-affected areas and cumulative MODIS active fire detections are illustrated and discussed for each fire regime. The results reveal a coherent spatio-temporal mapping of fire-affected area and indicate that the algorithm shows potential for global application.     

A parthenogenetic algorithm for single individual SNP haplotyping

The minimum error correction (MEC) model is one of the important computational models for single individual single nucleotide polymorphism (SNP) haplotyping. Due to the NP-hardness of the model, Qian et al. presented a particle swarm optimization (PSO) algorithm to solve it, and the particle code length is equal to the number of SNP fragments. However, there are hundreds and thousands of SNP fragments in practical applications. The PSO algorithm based on this kind of long particle code cannot obtain high reconstruction rate efficiently. In this paper, a practical heuristic algorithm PGA-MEC based on parthenogenetic algorithm (PGA) is presented to solve the model. A kind of short chromosome code and an effective recombination operator are designed for the algorithm. The reconstruction rate of PGA-MEC algorithm is higher than that of PSO algorithm and the running time of PGA-MEC algorithm is shorter than that of PSO algorithm, which are proved by a number of experiments.     

An algorithm for visible-line and visible-surface display of CSG models

An algorithm is presented for display of Constructive Solid Geometry (CSG) models, in which Boolean evaluation of a model is done during image generation only for the visible parts of the model.The algorithm is based on Atherton's CSG scan-line algorithm. It involves, however, dividing the image plane into strips of varying width, inside which areas are determined where only one face is visible. This may involve subdivision of an area into smaller areas.Two versions of the algorithm are presented: an efficient visible-line version for a raster display, and a visible-surface version, which turns out to be an improved variant for simple models of Atherton's algorithm.Sample images and CPU times for some models are given to show the efficiency of the algorithm.     

Region based techniques for segmentation of volumetric histo-pathological images

In this article we have presented the application of three region based segmentation techniques namely, seeded volume growing, constrained erosion-dilation techniques and 3-D watershed algorithm. The algorithms are suitably extended to apply on 3-D histo-pathological images. Suitable modifications and extension for each algorithm is done to obtain better segmentation. A quantitative as well as qualitative comparison of the three methods is presented. Modifications to these algorithms for obtaining better results are discussed. The modifications include, (1) design of adaptive similarity measures to control the seeded volume growing and (2) rule-based merging of the over-segmented cells in the case of the 3-D watershed algorithm. Some results and quantitative study is also presented.     

基于改进Sage滤波器的车辆行程时间预测模型

在现代ITS环境中,公交车辆行程时间预测是实现公共交通智能化调度子系统、电子站牌显示子系统及公交信息服务子系统的必要条件。针对Sage滤波器自身的优缺点,提出了一种基于车辆行程时间历史数据流信息的Sage滤波器,并在此基础建立了BRT（Bus Rapid Transit）车辆行程时间预测模型。最后针对2007年6月7日北京市南中轴路大容量快速公交（BRT）线的实际数据进行了对比实验,结果表明,改进的Sage滤波器有效降低了原算法的误差。     

基于蚁群信息机制的粒子群算法

针对粒子群算法应用于复杂函数优化时可能出现过早收敛于局部最优解的情况,提出了一种改进的算法。通过构造单个粒子的多个进化方向和类似于蚂蚁群算法信息素表的选择机制,保留了粒子的多种可能进化方向。提高了粒子间的多样性差异,从而改善算法能力。改进后的混合粒子群算法的性能优于带线性递减权重的粒子群算法。     

Orthogonal kernel machine for the prediction of functional sites in proteins.

A novel pattern recognition algorithm called an orthogonal kernel machine (OKM) is presented for the prediction of functional sites in proteins. Two novelties in OKM are that the kernel function is specially designed for measuring the similarity between a pair of protein sequences and the kernels are selected using the orthogonal method. Based on a set of well-recognized orthogonal kernels, this algorithm demonstrates its superior performance compared with other methods. An application of this algorithm to a real problem is presented.     

Optimality analysis of one-step OOSM filtering algorithms in target tracking

In centralized multisensor tracking systems, there are out-of-sequence measurements (OOSMs) frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrival can induce the “negative-time measurement update” problem, which is quite common in real multisensor tracking systems. The A1 optimal update algorithm with OOSM is presented by Bar-Shalom for one-step case. However, this paper proves that the optimality of A1 algorithm is lost in direct discrete-time model (DDM) of the process noise, it holds true only in discretized continuous-time model (DCM). One better OOSM filtering algorithm for DDM case is presented. Also, another new optimal OOSM filtering algorithm, which is independent of the discrete time model of the process noise, is presented here. The performance of the two new algorithms is compared with that of A1 algorithm by Monte Carlo simulations. The effectiveness and correctness of the two proposed algorithms are validated by analysis and simulation results.     

Dynamic steering control of conventionally steered mobile robots

This article presents an algorithm for dynamically steering an autonomous mobile robot (AMR) along a collision-free path to a goal using local feedback information. A twolevel navigation algorithm is presented in which a subgoal selection algorithm (SSA) generates visible subgoals to be pursued by the steering control algorithm (SCA). An earlier article presented the SSA in detail, demonstrating how subgoals can be updated while the AMR is moving so that a continuous motion is achieved without stopping to replan the path when new sensor data become available. This study focuses on the SCA. In particular, a general feedback scheme is developed for dynamically steering an AMR to a visible goal in the local obstacle-free space identified by the SSA. We present the detailed implementation of the SCA for a conventionally steered AMR (CAMR). Simulation results are presented that demonstrate the effectiveness of the combined SSA-SCA feedback scheme. The algorithm has been successfully implemented on the NavLab at CMU.     

基于客户满意度的开放式车辆路径问题研究

将客户满意度作为优化目标引入开放式车辆路径问题。使用梯形模糊数表示客户满意度,建立基于客户满意度的开放式车辆路径问题的数学模型。将改进的最邻近插入法和最廉价插入法作为后优化过程与粒子群优化算法结合求解该问题。分析2种混合算法的计算复杂度,通过实验仿真对算法进行分析比较。     

Parameter Incremental Learning Algorithm for Neural Networks

In this paper, a novel stochastic (or online) training algorithm for neural networks, named parameter incremental learning (PIL) algorithm, is proposed and developed. The main idea of the PIL strategy is that the learning algorithm should not only adapt to the newly presented input-output training pattern by adjusting parameters, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly presented as the first-order approximate solution to an optimization problem, where the performance index is the combination of proper measures of preservation and adaptation. The PIL algorithms for the multilayer perceptron (MLP) are subsequently derived. Numerical studies show that for all the three benchmark problems used in this paper the PIL algorithm for MLP is measurably superior to the standard online backpropagation (BP) algorithm and the stochastic diagonal Levenberg-Marquardt (SDLM) algorithm in terms of the convergence speed and accuracy. Other appealing features of the PIL algorithm are that it is computationally as simple as the BP algorithm, and as easy to use as the BP algorithm. It, therefore, can be applied, with better performance, to any situations where the standard online BP algorithm is applicable     

一种改进的支持向量机增量学习算法

提出了一种改进的支持向量机增量学习算法。分析了新样本加入后,原样本和新样本中哪些样本可能转化为新支持向量。基于分析结论提出了一种改进的学习算法。该算法舍弃了对最终分类无用的样本,并保留了有用的样本。对标准数据集的实验结果表明,该算法在保证分类准确度的同时大大减少了训练时间。     

Optimum load and resistance factor design of steel space frames using genetic algorithm

In this paper, an algorithm is presented for the minimum weight design of steel moment-resisting space frames subjected to American Institute of Steel Construction (AISC) Load and Resistance Factor Design (LRFD) specification. A genetic algorithm (GA) is utilized herein as the optimization method. Design variables which are cross-sectional areas are discrete and are selected from the standard set of AISC wide-flange (W) shapes. The structure is subjected to wind loading in accordance with the Uniform Building Code (UBC) in conjunction with vertical loads (dead and live loads). Displacement and AISC LRFD stress constraints are imposed on the structure. The algorithm is applied to the design of three space frame structures. The designs obtained using AISC LRFD code are compared to those where AISC Allowable Stress Design (ASD) is considered. The comparisons show that the former code results in lighter structures for the examples presented. Received November 15, 1999     

Optimal training subset in a support vector regression electric load forecasting model

This paper presents an optimal training subset for support vector regression (SVR) under deregulated power, which has a distinct advantage over SVR based on the full training set, since it solves the problem of large sample memory complexity O(N²) and prevents over-fitting during unbalanced data regression. To compute the proposed optimal training subset, an approximation convexity optimization framework is constructed through coupling a penalty term for the size of the optimal training subset to the mean absolute percentage error (MAPE) for the full training set prediction. Furthermore, a special method for finding the approximate solution of the optimization goal function is introduced, which enables us to extract maximum information from the full training set and increases the overall prediction accuracy. The applicability and superiority of the presented algorithm are shown by the half-hourly electric load data (48 data points per day) experiments in New South Wales under three different sample sizes. Especially, the benefit of the developed methods for large data sets is demonstrated by the significantly less CPU running time.     

关联规则的开采与更新

对关联规则的增量式更新算法——IUA(incremental updating algorithm)进行了分析,指出其存在的问题,并给出一个改进的算法NEWIUA(new IUA).NEWIUA算法对已存在的和本次更新时新产生的频繁项目集都加以充分的利用,因此,在保证算法有效的同时提高了效率.文章提出了3种关联规则开采的并行算法,并对各算法进行了分析和讨论.