Edge, junction, and corner detection using color distributions

For over 30 years (1970-2000) researchers in computer vision have been proposing new methods for performing low-level vision tasks such as detecting edges and corners. One key element shared by most methods is that they represent local image neighborhoods as constant in color or intensity with deviations modeled as noise. Due to computational considerations that encourage the use of small neighborhoods where this assumption holds, these methods remain popular. The research presented models a neighborhood as a distribution of colors. The goal is to show that the increase in accuracy of this representation translates into higher-quality results for low-level vision tasks on difficult, natural images, especially as neighborhood size increases. We emphasize large neighborhoods because small ones often do not contain enough information. We emphasize color because it subsumes gray scale as an image range and because it is the dominant form of human perception. We discuss distributions in the context of detecting edges, corners, and junctions, and we show results for each     

Decision modeling for foreclosed property acquisition by community development corporations in the United States

Over the past several years, increased rates of mortgage foreclosures in the United States have had significant impacts on economies around the world. As part of the societal response to this problem, nonprofit community development corporations acquire foreclosed properties in troubled neighborhoods and redevelop them to maintain stability. This paper addresses a decision problem faced by these organizations on what types of properties to acquire given limited available resources. We develop dynamic and stochastic programming models to assist such organizations in choosing foreclosed properties for acquisition and redevelopment, taking into account uncertain market conditions that change over time. We first analytically study a dynamic programming model under some special cases, and then seek insights for the general stochastic model through a multistage stochastic programming formulation and its numerical analysis. Although our analysis is based on a problem in the United States with potential implications for the global economy, our research is applicable to non‐U.S. government actors at the local and regional level as well.     

Using landscape typologies to model socioecological systems: Application to agriculture of the United States Gulf Coast

The complexity of socioecological systems (SES) has posed a persistent challenge to the development of methods for diagnostic and prognostic analyses of global change. We developed a high dimensional statistical framework where cluster analysis was used to characterize regional landscape typologies and those typologies are linked to the outcome of interest through regression modeling. For demonstration, we applied the framework to agroecosystem of the United States Gulf Coast to evaluate the determinants of spatial variability in crop yield. Regional biophysical typologies (BPT; integrated climate, soil, and topography clusters) and socioecological typologies (SET; BPT combined with socioeconomic clusters) were developed. The SET corn model (R2 = 0.89) outperformed the BPT corn model (R2 = 0.72) and a county fixed-effect model (R2 = 0.53), which reflects the socioeconomic influence over agricultural productivity. The SET model also showed similar predictive skill for soybean and cotton yield. Therefore impact analysis for agroecosystems can lead to incorrect conclusions if biophysical factors are not examined jointly with socioeconomic factors.     

Emergent Groups in Established Frameworks: Ottawa Carleton's Response to the 1998 Ice Disaster

In his book, Organized Behavior in Disaster, Russell Dynes classifies organizations that respond to emergencies four ways – regular, expanding, extending and emergency. He bases this on an analysis of research done mainly in the United States. This article examines whether these typologies fit a 1998 ice storm that left about one-fifth of Canadians without power. The conclusion is that the typologies do fit. However, in the case of the ice storm the ‘emergent’ groups were formed from within rather than outside the established response structure. As a result, there was none of the expected conflict between existing and emergent organizations. The lesson for planners is that if they adapt rapidly to changing circumstances and are ready to sponsor or include emergent groups in their existing structure they can reduce or eliminate conflict.     

Automated approaches to characterizing educational digital library usage: linking computational methods with qualitative analyses

The need for automatic methods capable of characterizing adoption and use has grown in operational digital libraries. This paper describes a computational method for producing two, inter-related, user typologies based on use diffusion. Furthermore, a case study is described that demonstrates the utility and applicability of the method: it is used to understand how middle and high school science teachers participating in an academic year-long field trial adopted and integrated digital library resources into their instructional planning and teaching. Use diffusion theory views technology adoption as a process that can lead to widely different patterns of use across a given population of potential users; these models use measures of frequency and variety to characterize and describe such usage patterns. By using computational techniques such as clickstream entropy and clustering, the method produces both coarse- and fine-grained user typologies. As a part of improving the initial coarse-grain typology, clickstream entropy improvements are described that aim at better separation of users. In addition, a fine-grained user typology is described that identifies five different types of teacher-users, including ??interactive resource specialists?? and ??community seeker specialists.?? This typology was validated through comparison with qualitative and quantitative data collected using traditional educational field research methods. Results indicate that qualitative analyses correlate with the computational results, suggesting automatic methods may prove an important tool in discovering valid usage characteristics and user types.     

Auto-SOM: recursive parameter estimation for guidance of self-organizing feature maps

An important technique for exploratory data analysis is to form a mapping from the high-dimensional data space to a low-dimensional representation space such that neighborhoods are preserved. A popular method for achieving this is Kohonen's self-organizing map (SOM) algorithm. However, in its original form, this requires the user to choose the values of several parameters heuristically to achieve good performance. Here we present the Auto-SOM, an algorithm that estimates the learning parameters during the training of SOMs automatically. The application of Auto-SOM provides the facility to avoid neighborhood violations up to a user-defined degree in either mapping direction. Auto-SOM consists of a Kalman filter implementation of the SOM coupled with a recursive parameter estimation method. The Kalman filter trains the neurons' weights with estimated learning coefficients so as to minimize the variance of the estimation error. The recursive parameter estimation method estimates the width of the neighborhood function by minimizing the prediction error variance of the Kalman filter. In addition, the "topographic function" is incorporated to measure neighborhood violations and prevent the map's converging to configurations with neighborhood violations. It is demonstrated that neighborhoods can be preserved in both mapping directions as desired for dimension-reducing applications. The development of neighborhood-preserving maps and their convergence behavior is demonstrated by three examples accounting for the basic applications of self-organizing feature maps.     

Sequential variable neighborhood descent variants: an empirical study on the traveling salesman problem

In a single local search algorithm, several neighborhood structures are usually explored. The simplest way is to define a single neighborhood as the union of all predefined neighborhood structures; the other possibility is to make an order (or sequence) of the predefined neighborhoods, and to use them in the first improvement or the best improvement fashion, following that order. In this work, first we classify possible variants of sequential use of neighborhoods and then, empirically analyze them in solving the classical traveling salesman problem (TSP). We explore the most commonly used TSP neighborhood structures, such as 2‐opt and insertion neighborhoods. In our empirical study, we tested 76 different such heuristics on 15,200 random test instances. Several interesting observations are derived. In addition, the two best of 76 heuristics (used as local searches within a variable neighborhood search) are tested on 23 test instances taken from the TSP library (TSPLIB). It appears that the union of neighborhoods does not perform well.     

Towards robust topology of sparsely sampled data

Sparse, irregular sampling is becoming a necessity for reconstructing large and high-dimensional signals. However, the analysis of this type of data remains a challenge. One issue is the robust selection of neighborhoods--a crucial part of analytic tools such as topological decomposition, clustering and gradient estimation. When extracting the topology of sparsely sampled data, common neighborhood strategies such as k-nearest neighbors may lead to inaccurate results, either due to missing neighborhood connections, which introduce false extrema, or due to spurious connections, which conceal true extrema. Other neighborhoods, such as the Delaunay triangulation, are costly to compute and store even in relatively low dimensions. In this paper, we address these issues. We present two new types of neighborhood graphs: a variation on and a generalization of empty region graphs, which considerably improve the robustness of neighborhood-based analysis tools, such as topological decomposition. Our findings suggest that these neighborhood graphs lead to more accurate topological representations of low- and high- dimensional data sets at relatively low cost, both in terms of storage and computation time. We describe the implications of our work in the analysis and visualization of scalar functions, and provide general strategies for computing and applying our neighborhood graphs towards robust data analysis.     

Early computer graphics developments in the architecture,engineering and construction industry

Despite 30 years' experimentation and 20 years' availability of commercial products, the architecture, engineering, and construction industry in the mid-1990s had yet to achieve an effective integration of computer-based techniques into its business processes. Business processes in all industries are resistant to change, and people tend to use new tools in the same way they used their old ones: computers as pencils. In the architecture, engineering, and construction industry within the United States, this tendency has been aggravated by the segmentation of the work process into myriad specialties, frequently performed by separate companies, with the information flow obstructed by professional licensing, regulation, contracts, the profit motive, and even the training of design professionals. However, a number of developments-the emergence of object technology; industry standardization initiatives; widespread adoption of Internet technologies; and competitive pressures-are converging to create both the feasibility of and the necessity for rethinking and restructuring the industry. The article focuses on computer graphics precedents related to the architecture, engineering, and construction industry     

Incorporating language syntax in visual text recognition with astatistical model

The use of a statistical language model to improve the performance of an algorithm for recognizing digital images of handwritten or machine-printed text is discussed. A word recognition algorithm first determines a set of words (called a neighborhood) from a lexicon that are visually similar to each input word image. Syntactic classifications for the words and the transition probabilities between those classifications are input to the Viterbi algorithm. The Viterbi algorithm determines the sequence of syntactic classes (the states of an underlying Markov process) for each sentence that have the maximum a posteriori probability, given the observed neighborhoods. The performance of the word recognition algorithm is improved by removing words from neighborhoods with classes that are not included on the estimated state sequence. An experimental application is demonstrated with a neighborhood generation algorithm that produces a number of guesses about the identity of each word in a running text. The use of zero, first and second order transition probabilities and different levels of noise in estimating the neighborhood are explored     

On Node Criticality in Air Transportation Networks

In this study, we analyze the criticality of nodes in air transportation using techniques from three different domains, and thus, three essentially different perspectives of criticality. First, we examine the unweighted structure of air transportation networks, using recent methods from control theory (maximum matching and minimum dominating set). Second, complex network metrics (betweenness and closeness) are used with passenger traffic as weights. Third, ticket data-level analysis (origin-destination betweenness and outbound traffic with transit threshold) is performed. Remarkably, all techniques identify a different set of critical nodes; while, in general, giving preference to the selection of high-degree nodes. Our evaluation on the international air transportation country network suggests that some countries, e.g., United States, France, and Germany, are critical from all three perspectives. Other countries, e.g., United Arab Emirates and Panama, have a very specific influence, by controlling the passenger traffic of their neighborhood countries. Furthermore, we assess the criticality of the country network using Multi-Criteria Decision Analysis (MCDA) techniques. United States, Great Britain, Germany, and United Arab Emirates are identified as non-dominated countries; Sensitivity analysis shows that United Arab Emirates is most sensitive to the preference information on the outbound traffic. Our work gears towards a better understanding of node criticality in air transportation networks. This study also stipulates future research possibilities on criticality in general transportation networks.     

全球变化空间观测研究的文献计量分析

全球变化研究是国际科学界跨世纪的重大研究主题。随着全球环境问题的日益突出,全球变化研究日益得到国际科学界和国际社会的高度关注。空间观测是全球变化研究的一种不可或缺的重要方法,在全球变化研究中发挥着不可替代的重要作用。科技文献能够反映科学研究的发展动态,利用Thomson Data Anlyzer(TDA)和Ucinet对SCIE文献数据库中收录的1980~2009年间的全球变化空间观测研究论文进行数据挖掘和分析。结果表明,该研究涉及多个学科领域,大量研究始于1990年,气候变化、碳循环、土地利用变化等是该领域的主要研究内容,遥感、建模等是主要的研究方法,美国在该研究领域具有很大优势。     

The influence of built environment features on crowdsourced physiological responses of pedestrians in neighborhoods

With the growth of interest in walkable neighborhoods, various efforts have been made to investigate to determine what kinds of built environment features induce physical and physiological discomfort in pedestrians in a neighborhood. Traditional evaluation approaches primarily rely on opinion surveys and field observation (e.g., neighborhood surveys and visual inspection) completed by pedestrians and trained auditors respectively, both of which require considerable time and funding. Additionally, visual audit and opinion survey methods are not free from subjectivity concerns. In this paper, we propose and test a novel approach to assess conditions of walkable environment by using body responses. The paper utilizes crowdsourced physiological data from pedestrians (e.g., gait stability, gait acceleration, and relative heart rate) to examine the interaction between built environment features and pedestrians' physical activities in a neighborhood. In an experiment conducted in Havelock neighborhood of Lincoln, Nebraska, subjects were asked to walk a pre-defined path of 1.26 km while bearing a wearable inertial measurement units (IMU) sensor, a wristband-type wearable device, and a smartphone. Additionally, subjects were asked to provide a subjective assessment of subsegments on a scale of 0 to 10. With these data, we investigate the relationship between physiological responses and the existing built environment features encountered by subjects. Our findings indicate that physiological response has a statistically significant relationship with built environment features and subjective ratings. The outcomes of the research will help improve the evaluation methods of built environment features and will promote neighborhood walkability.     

Main Effect Fine-tuning of the Mutation Operator and the Neighbourhood Function for Uncapacitated Facility Location Problems

In both genetic algorithms (GAs) and simulated annealing (SA), solutions can be represented by gene representation. Mutation operator in GA and neighborhood function in SA are used to explore the solution space. They usually select genes for performing mutation. The rate of selection of genes can be called mutation rate. However, randomly selecting genes may not be the best way for both algorithms. This paper describes how to estimate the main effect in genes representation. The resulting estimates cannot only be used to understand the domination of gene representation, but also employed to fine-tune the mutation rate in both the mutation operator in the GA and the neighborhood function in the SA. It has been demonstrated the use of the proposed methods for solving uncapacitated facility location problems and discuss the examination of the proposed methods with some useful comparisons with both the latest developed GA and SA for solving this problem. For many well-known benchmark problems, the proposed methods yield better results in solution quality than the previously used methods.     

A scheduling problem in blocking hybrid flow shop robotic cells with multiple robots

This paper addresses the robotic scheduling problem in blocking hybrid flow shop cells that consider multiple part types, unrelated parallel machines, multiple robots and machine eligibility constraints. Initially, a mixed integer linear programming (MILP) model is proposed to minimize the makespan for this problem. Due to the complexity of the model, a simulated annealing (SA) based solution approach is developed for its solution. To increase the efficiency of the SA algorithm, a new neighborhood structure based on block properties is applied. The performance of the proposed SA is assessed over a set of randomly generated instances. The computational results demonstrate that the SA algorithm is effective with the employed neighborhood structure. Additionally, this study shows that the appropriate number of robots depends on the sequence of processing operations to be performed at each stage.     

An epidemiological assessment of online groups and a test of a typology: What are the (dis)similarities of the online group types?

A vast multitude of online groups exist, and authors have been rapidly investigating their dynamics. Extant studies have provided great information on the effects of online group membership, but limitations are often noted in these studies. Amongst the most concerning limitations are issues of generalizability. Authors are often unsure whether their results are able to generalize to other online groups, including those that are seemingly similar. For this reason, some researchers have created typologies of online groups, in hopes that online groups that fall within the same category will be generalizable; however, no study has analyzed the merit of an online group typology, and conclusions are based upon speculation. For this reason, the current study analyzed the dynamics of three different online groups, which fall within separate categories of an online group typology: a cancer support forum, a LGBT forum, and a Harry Potter fan forum. The results demonstrate that these groups vary in their properties, including group members’ group identity, well-being, and social support. These results provide support for an online group typology, and precisely demonstrate in what manner these groups differ. Additionally, the results offer valuable information about the individual groups, as some variables were previously unstudied in some group types. The discovery of these previously unknown dynamics leads to the potential of new studies, which is discussed. Therefore, the current study provides important implications for future studies, as well as the interpretation of future research results.     

混合粒子群算法求解带软时间窗的VRPSPD问题

针对带软时间窗的同时集配货车辆路径问题（VRPSPD）,建立了以车辆派遣成本、行驶成本和时间窗惩罚成本之和最小为目标的车辆路径优化模型;设计混合粒子群算法进行求解,该算法结合以变邻域下降搜索为主体的适应性扰动机制,采用适应性选择邻域策略,并在每个邻域搜索中应用可变的循环次数,以此提高对解空间的探测能力和搜索效率。数值实验结果表明了该算法的可行性和有效性。     

VLSI平面布图规划中模拟退火算法的加速策略

布局是现代VLSI物理设计中十分关键的步骤,而模拟退火等智能算法在针对宏模块布局的平面布图规划问题中得到广泛应用。针对应用于VLSI平面布图规划的模拟退火算法进行了研究和分析,并针对布图本身特性在退火算法中采用了一种导向性的邻域构造策略来加速算法的收敛,有效地提高了平面布图规划中模拟退火算法的搜索效率。