Constraint-based probabilistic modeling for statistical abduction

We introduce a new framework for logic-based probabilistic modeling called constraint-based probabilistic modeling which defines CBPMs (constraint-based probabilistic models) , i.e. conditional joint distributions P(⋅∣KB) over independent propositional variables constrained by a knowledge base KB consisting of clauses. We first prove that generative models such as PCFGs and discriminative models such as CRFs have equivalent CBPMs as long as they are discrete. We then prove that CBPMs in infinite domains exist which give existentially closed logical consequences of KB probability one. Finally we derive an EM algorithm for the parameter learning of CBPMs and apply it to statistical abduction.     

Constraint-based metaview approach for modeling environment generation

Modeling environments for decision support systems (DSS's) play an important role in improving the efficiency and effectiveness of model-related work. The task of developing such an environment, however, is complicated and time-consuming. To improve the productivity and quality involved in the development of modeling environments, a metaview approach for generating these environments is proposed in this paper. Specifically, a constraint-based modeling framework is designed to specify modeling environments and associated modeling activities. The framework is based on a metaview that any modeling environment is a constraint enforcement system. A prototype environment generator called MetaDSS has been implemented. The practical generation of a modeling environment, a structured modeling environment specifically, is demonstrated using the MetaDSS.     

Augmented scene modeling and visualization by optical and acoustic sensor integration

In this paper, underwater scene modeling from multisensor data is addressed. Acoustic and optical devices aboard an underwater vehicle are used to sense the environment in order to produce an output that is readily understandable even by an inexperienced operator. The main idea is to integrate multiple-sensor data by geometrically registering such data to a model. The geometrical structure of this model is a priori known but not ad hoc designed for this purpose. As a result, the vehicle pose is derived and model objects can be superimposed upon actual images, thus generating an augmented-reality representation. Results on a real underwater scene are reported, showing the effectiveness of the proposed approach.     

Constraint-based modeling of discrete event dynamic systems

Numerous frameworks dedicated to the modeling of discrete event dynamic systems have been proposed to deal with programming, simulation, validation, situation tracking, or decision tasks: automata, Petri nets, Markov chains, synchronous languages, temporal logics, event and situation calculi, STRIPS…All these frameworks present significant similarities, but none offers the flexibility of more generic frameworks such as logic or constraints. In this article, we propose a generic constraint-based framework for the modeling of discrete event dynamic systems, whose basic components are state, event, and time attributes, as well as constraints on these attributes, and which we refer to as CNT for Constraint Network on Timelines. The main strength of such a framework is that it allows any kind of constraint to be defined on state, event, and time attributes. Moreover, its great flexibility allows it to subsume existing apparently different frameworks such as automata, timed automata, Petri nets, and classical frameworks used in planning and scheduling.     

服装用图形限制关系描述语言-GCGL

文章设计了一种服装用图元限制关系描述语言GCGL。该语言是个体裁剪方式下,自动化修正模板衣片纸样时所需的重要工具。文章对GCGL的语法和操作语义进行了定义和描述。语言的基本图元定义简单,图元和限制规则的复合能力很强,语义定义容易理解和掌握。GCGL在实践应用过程中表现出很强的可扩展性和可移植性。     

Constraint-based software for concurrent engineering

The Galileo3 programming language for developing product life-cycle design advice software is described. Galileo3's various application-oriented features, the algorithm on which the runtime system is based, and its underlying computational theory are discussed     

Visual graph modeling for scene recognition and mobile robot localization

Image retrieval and categorization may need to consider several types of visual features and spatial information between them (e.g., different point of views of an image). This paper presents a novel approach that exploits an extension of the language modeling approach from information retrieval to the problem of graph-based image retrieval and categorization. Such versatile graph model is needed to represent the multiple points of views of images. A language model is defined on such graphs to handle a fast graph matching. We present the experiments achieved with several instances of the proposed model on two collections of images: one composed of 3,849 touristic images and another composed of 3,633 images captured by a mobile robot. Experimental results show that using visual graph model (VGM) improves the accuracies of the results of the standard language model (LM) and outperforms the Support Vector Machine (SVM) method.     

Computational model for recognition of scene objects with an active sensor

In the paper, a computational model for recognition of objects in a scene image is presented. The model is based on the use of an active sensor. The structure of the object model (OM) is described. This structure is a component that stores different representations of the object and puts at user’s disposal an interface whose operations are used in the scene recognition process. Semen Yu. Sergunin. Born 1980. Graduated from the Faculty of Mathematics and Mechanics of Moscow State University in 2002. Finished postgraduate course of the Department of Computational Mathematics of the same faculty. Scientific interests include image recognition. Author of about 20 papers. Mikhail I. Kumskov. Graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University in 1978. Received his candidate’s degree (in Physics and Mathematics) in 1981 and doctoral degree in 1997. In 1981–1997 taught at the Faculty of Computational Mathematics and Cybernetics in the special seminar on Computer Graphics and Image Processing of the Department of Automation of Systems of Computational Complexes. Since 1992 works at the laboratory of Mathematical Chemistry of the Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences. Since 1997 teaches at the Department of Computational Mathematics of the Faculty of Mathematics and Mechanics of Moscow State University. Author of more than 50 papers. Scientific interests include prediction of properties of chemical compounds, optimization of structural object representation for classification problems, and image understanding.     

Decision-theoretic cooperative sensor planning

This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPA's Unmanned Ground Vehicle program     

视点依赖的温室场景模拟与交互式漫游

为对包含日光温室的农业场景进行实时、逼真的绘制,采用基于视点的连续细节层次（LOD）来减少实际需要绘制的网格数据量,并基于包围盒技术实现了视点的碰撞检测,同时使用可见性剔除算法来加速场景渲染速度。在渲染温室内植物时,通过几何变换实现了大规模植物群体的快速构建,并在场景中使用阴影体算法来渲染阴影,生成了具有真实感的大规模温室农业场景。模拟结果表明该方法能有效地减少渲染的面元数目,大大提高了绘制速率,场景有较高的真实感,能够满足交互式实时漫游的要求。     

虚拟雪景物理建模与仿真

基于动力学原理建立了雪花飘落的简化运动模型,并在D3D技术基础上,基于粒子系统的原理,绘制渲染了雪花纷飞的动态虚拟场景。提出了简化粒子发射器模型和碰撞检测方法,并对风力模型作了近似,在保证仿真效果的基础上提高了运算速度。实验结果表明,该方法仿真效果较好,速度快,同时能容易地推广应用到其他诸如雨、烟、雾等虚拟场景的绘制中。     

Perception modeling for human-like artificial sensor systems

In this article we present an approach to the design of human-like artificial systems. It uses a perception model to describe how sensory information is processed for a particular task and to correlate human and artificial perception. Since human-like sensors share their principle of operation with natural systems, their response can be interpreted in an intuitive way. Therefore, such sensors allow for easier and more natural human–machine interaction.The approach is demonstrated in two applications. The first is an “electronic tongue”, which performs quality assessment of food and water. In the second application we describe the development of an artificial hand for dexterous manipulation. We show that human-like functionality can be achieved even if the structure of the system is not completely biologically inspired.     

Constraint-based document layout for the Web

Constraints can be used to specify declaratively the desired layout of a Web document. We present a system architecture in which both the author and the viewer can impose page layout constraints, some required and some preferential. The final appearance of the Web page is thus the result of negotiation between author and viewer, where this negotiation is carried out by solving the set of required and preferential constraints imposed by both parties. We identify two plausible system architectures, based on different ways of dividing the work of constraint solving between Web server and Web client. We describe a prototype constraint-based Web authoring system and viewing tool that provides linear arithmetic constraints for specifying the layout of the document as well as finite-domain constraints for specifying font size relationships. Finally, we provide an empirical evaluation of the prototype.     

A mixed spaceborne sensor approach for surface modelling of an urban scene

Three-dimensional (3D) surface models are vital for sustainable urban management studies, and there is a nearly unlimited range of possible applications. Along- or across-track pairs from the same set of sensor imagery may not always be available or economical for a certain study area. Therefore, a photogrammetric approach is proposed in which a digital surface model (DSM) is extracted from a stereo pair of satellite images, acquired by different sensors. The results demonstrate that a mixed-sensor approach may offer a sound alternative to the more established along-track pairs. However, one should consider several criteria when selecting a suitable stereo pair. Two cloud-free acquisitions are selected from the IKONOS and QuickBird image archives, characterized by sufficient overlap and optimal stereo constellation in terms of complementarity of the azimuth and elevation angles. A densely built-up area in Istanbul, Turkey, covering 151 km² and with elevations ranging between sea level and approximately 160 m is presented as the test site. In addition to the general complexity of modelling the surface and elevation of an urban environment, multi-sensor image fusion has other particular difficulties. As the images are acquired from a different orbital pass, at a different date or instant and by a different sensor system, radiometric and geometric dissimilarities can occur, which may hamper the image-matching process. Strategies are presented for radiometric and geometric normalization of the multi-temporal and multi-sensor imagery and to deal with the differences in sensor characteristics. The accuracy of the generated surface model is assessed in comparison with 3D reference points, 3D rooftop vector data and surface models extracted from an along-track IKONOS stereo pair and an IKONOS triplet. When compared with a set of 35 reference GPS check points, the produced mixed-sensor model yields accuracies of 1.22, 1.53 and 2.96 m for the X, Y and Z coordinates, respectively, expressed in terms of root mean square errors (RMSEs). The results show that it is feasible to extract the DSM of a highly urbanized area from a mixed-sensor pair, with accuracies comparable with those observed from the DSM extracted from an along-track pair. Hence, the flexibility of reconstructing valuable elevation models is greatly increased by considering the mixed-sensor approach.     

Constraint-based LN curves

We consider the design of parametric curves from geometric constraints such as distance from lines or points and tangency to lines or circles. We solve the Hermite problem with such additional geometric constraints. We use a family of curves with linearly varying normals, LN curves. The nonlinear equations that arise can be of algebraic degree 60. We solve them using the GPU on commodity graphics cards and achieve interactive performance. The family of curves considered has the additional property that the convolution of two curves in the family is again a curve in the family, assuming common Gauss maps, making the class more useful to applications. Further, we consider valid ranges in which the line tangency constraint can be imposed without the curve segment becoming singular. Finally, we remark on the larger class of LN curves and how it relates to Bézier curves.     

Constraint-based clustering selection

Clustering requires the user to define a distance metric, select a clustering algorithm, and set the hyperparameters of that algorithm. Getting these right, so that a clustering is obtained that meets the users subjective criteria, can be difficult and tedious. Semi-supervised clustering methods make this easier by letting the user provide must-link or cannot-link constraints. These are then used to automatically tune the similarity measure and/or the optimization criterion. In this paper, we investigate a complementary way of using the constraints: they are used to select an unsupervised clustering method and tune its hyperparameters. It turns out that this very simple approach outperforms all existing semi-supervised methods. This implies that choosing the right algorithm and hyperparameter values is more important than modifying an individual algorithm to take constraints into account. In addition, the proposed approach allows for active constraint selection in a more effective manner than other methods.     

Constraint-based motion adaptation

Today's computer animators have access to many systems and techniques to author high-quality motion. Unfortunately, available techniques typically produce a particular motion for a specific character. In this paper we present a constraint-based approach to adapt previously created motions to new situations and characters. We combine constraint methods that compute changes to motion to meet specified needs with motion signal processing methods that modify signals yet preserve desired properties of the original motion. The combination allows the adaptation of motions to meet new goals while retaining much of the motion's original quality. © 1998 John Wiley & Sons, Ltd.