Visual spatio-temporal function-based querying

Visual interfaces are very important for human interactions in cyberworlds. Visual spatio-temporal querying should be one of the basic tools for data mining and retrieval in cyberworlds. In this paper, we propose a novel function-based query model for arbitrary shape spatio-temporal querying. The queries are defined as geometric shapes changing over time. In our model, data are interpreted geometrically as multidimensional points with time dimension or as moving points. The queries are formulated with geometric objects and operations over them to form the query solid changing over time. The proposed query model allows us to pose arbitrary shape spatio-temporal range queries. With the uniform geometric model we integrate visual mining and querying of time-dependent data employing 3D visualization tools. It allows for creating an intuitive visual interface using 2D projections of 3D query shapes. Our approach combines visualization of spatio-temporal data with visualization of the range query formulation employing very compact function-based query model. The implemented visual query system and its visual interface are proposed and described. An example of application of the system in analysis of simulation results in molecular dynamics is considered.     

Development of holonic manufacturing execution systems

Rapid changes of market demands and pressures of competition require manufacturers to maintain highly flexible manufacturing systems to cope with a complex manufacturing environment. To meet these requirements, this work adopts the concepts of holon and holarchy to design manufacturing systems. Holon and holarchy are derived from the studies of social organizations and living organisms and possess the properties of intelligence, autonomy, cooperation, reconfigurability, and extensibility. Moreover, advanced manufacturing systems also require the properties of security certification and failure recovery. Based on the requirements of these properties, a systematic approach is proposed to develop a holonic manufacturing execution system (HMES) for the semiconductor industry. This systematic approach starts with a system analysis by collecting domain requirements and analyzing domain knowledge. The HMES Holarchy is designed by the procedure of constructing an abstract object model based on domain knowledge, partitioning application domain into functional holons, identifying generic parts among functional holons, developing the Generic Holon, defining holarchy messages and the holarchy framework of HMES, and finally designing functional holons based on the Generic Holon. It is believed that this proposed systematic approach provides a novel and efficient way to design HMES.     

Object detection,shape recovery,and 3D modelling by depth-encoded hough voting

Detecting objects, estimating their pose, and recovering their 3D shape are critical problems in many vision and robotics applications. This paper addresses the above needs using a two stages approach. In the first stage, we propose a new method called DEHV – Depth-Encoded Hough Voting. DEHV jointly detects objects, infers their categories, estimates their pose, and infers/decodes objects depth maps from either a single image (when no depth maps are available in testing) or a single image augmented with depth map (when this is available in testing). Inspired by the Hough voting scheme introduced in [1], DEHV incorporates depth information into the process of learning distributions of image features (patches) representing an object category. DEHV takes advantage of the interplay between the scale of each object patch in the image and its distance (depth) from the corresponding physical patch attached to the 3D object. Once the depth map is given, a full reconstruction is achieved in a second (3D modelling) stage, where modified or state-of-the-art 3D shape and texture completion techniques are used to recover the complete 3D model. Extensive quantitative and qualitative experimental analysis on existing datasets [2], [3], [4] and a newly proposed 3D table-top object category dataset shows that our DEHV scheme obtains competitive detection and pose estimation results. Finally, the quality of 3D modelling in terms of both shape completion and texture completion is evaluated on a 3D modelling dataset containing both in-door and out-door object categories. We demonstrate that our overall algorithm can obtain convincing 3D shape reconstruction from just one single uncalibrated image.     

Knowledge acquisition using hypertext

This paper addresses the issue of supporting knowledge acquisition using hypertext. We propose a way of tightly integrating hypertext and structured object representation, using Artificial Intelligence (AI) frames for the basic representation of hypertext nodes. Epistemologically, a dual view of the resulting space is of interest. One view is that of hypertext which emphasizes nodes containg g text, including formal knowledge representation. The other view focuses on objects with certain relationships, which define a semantic network. Both in hypertext and in semantic networks the relations between chunks of knowledge are explicitly represented by links. However, in today's hypertext systems a node typically contains just informal text and references to other nodes. Our approach additionally facilitates the explicit representation of structure “inside” hypertext nodes using partitions. We show the usefulness of such a tight integration for knowledge acquisition, providing several features useful for supporting it based on a level of basic hypertext functionality. In particular, we sketch a method for doing knowledge acquisition in such an environment. Hypertext is used as a mediating “semiformal” representation, which allows experts to directly represent knowledge without the immediate support of knowledge engineers. These help then to make this knowledge operational, supported by the system's facility to provide templates as well as their links to the semiformal representation. As an example of our results of using this method of knowledge acquisition, we illustrate the strategic knowledge in our application domain. More generally, our approach supports important aspects of (software) engineering knowledge-based systems and their maintenance. Also their user interface can be improved this way.     

From Web Data to Visualization via Ontology Mapping

In this paper, we propose a novel approach for automatic generation of visualizations from domain‐specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages.     

A semantic framework and software design to enable the transparent integration,reorganization and discovery of natural systems knowledge

I present a conceptualization that attempts to unify diverse representations of natural knowledge while providing a workable computational framework, based on current semantic web theory, for developing, communicating, and running integrated simulation models. The approach is based on a long-standing principle of scientific investigation: the separation of the ontological character of the object of study from the semantics of the observation context, the latter including location in space and time and other observation-related aspects. I will show how current Knowledge Representation theories coupled with the object-oriented paradigm allow an efficient integration through the abstract model of a domain, which relates to the idea of aspect in software engineering. This conceptualization allows us to factor out two fundamental causes of complexity and awkwardness in the representation of knowledge about natural system: (a) the distinction between data and models, both seen here as generic knowledge sources; (b) the multiplicity of states in data sources, handled through the hierarchical composition of independently defined domain objects, each accounting for all states in one well-known observational dimension. This simplification leaves modelers free to work with the bare conceptual bones of the problem, encapsulating complexities connected to data format, and scale. I will then describe the design of a software system that implements the approach, referring to explicit ontologies to unambiguously characterize the semantics of the objects of study, and allowing the independent definition of a global observation context that can be redefined as required. I will briefly discuss applications to multi-scale, multi-paradigm modeling, intelligent database design, and web-based collaboration.     

面向功能的表达系统HAR

HAR(Hypothesis Associative Representation)系统是一个面向功能的表达系统,由三部分组成:表达语言HARL、知识库管理子系统和推理机.HAR系统提供了三级描述和假设联想功能,把领域知识的表达和运用知识的推理控制策略紧密地结合在一起,实现对子问题求解的功能化表达.     

单像机有源形状恢复方法研究

物体表面三维形状恢复是计算机视觉的一个重要研究内容.给出了一种利用网格结 构光,采用单像机恢复物体表面三维形状的方法.提出了一种新的定标方法和投影模板检测 算法,并构造完成了有源三维重建实验系统.结果表明该方法能快速、准确地恢复物体形状.     

Knowledge-based artificial neural networks

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset information missing from the other source. By so doing, a hybrid learning system should learn more effectively than systems that use only one of the information sources. KBANN (Knowledge-Based Artificial Neural Networks) is a hybrid learning system built on top of connectionist learning techniques. It maps problem-specific “domain theories”, represented in propositional logic, into neural networks and then refines this reformulated knowledge using backpropagation. KBANN is evaluated by extensive empirical tests on two problems from molecular biology. Among other results, these tests show that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several techniques proposed by biologists.     

Shape-from-recognition: Recognition enables meta-data transfer

Low-level cues in an image not only allow to infer higher-level information like the presence of an object, but the inverse is also true. Category-level object recognition has now reached a level of maturity and accuracy that allows to successfully feed back its output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer different kinds of meta-data annotations for images of previously unseen object instances, in particular information on 3D shape. Meta-data can be discrete, real- or vector-valued. Our approach builds on the Implicit Shape Model of Leibe and Schiele [B. Leibe, A. Leonardis, B. Schiele, Robust object detection with interleaved categorization and segmentation, International Journal of Computer Vision 77 (1–3) (2008) 259–289], and extends it to transfer annotations from training images to test images. We focus on the inference of approximative 3D shape information about objects in a single 2D image. In experiments, we illustrate how our method can infer depth maps, surface normals and part labels for previously unseen object instances.     

Round-Eye: A system for tracking nearest surrounders in moving object environments

This paper presents “Round-Eye”, a system for tracking nearest surrounding objects (or nearest surrounders) in moving object environments. This system provides a platform for surveillance applications. The core part of this system is continuous nearest surrounder (NS) query that maintains views of the nearest objects at distinct angles from query points. This query differs from conventional spatial queries such as range queries and nearest neighbor queries as NS query considers both distance and angular aspects of objects with respect to a query point at the same time. In our system framework, a centralized server is dedicated (1) to collect location updates of both objects and queries, (2) to determine which NS queries are invalidated in presence of object/query location changes and corresponding result changes if any, and (3) to refresh the affected query answers. To enhance the system performance in terms of processing time and network bandwidth consumption, we propose various techniques, namely, safe region, partial query reevaluation, and incremental query result update. Through simulations, we evaluate our system with the proposed techniques over a wide range of settings.     

A hand-held approach to 3D reconstruction using light stripe projections onto a cube frame

The current paper presents a new light-striping approach for reconstructing a 3D model from a real object. The proposed system consists of a light plane projector, camera, and cube frame with LEDs attached. Instead of a strictly controlled camera and light emitters, a freely movable hand-held device is used that enables one to scan self-occluded objects. As in other light-striping systems, the correspondence problem is solved by projecting a light plane onto an object inside a frame. In the proposed system, the 3D coordinates of an illuminated light stripe are obtained using a calibration-free approach or dynamic optical triangulation. Furthermore, the computed 3D point data does not require any registration process because the data is directly measured based on unified world coordinates. Experimental results proved the accuracy of the measurements and consistency of the outcomes without any knowledge of the camera and light source parameters. An erratum to this article can be found at     

An architecture for software that adapts to changes in requirements

The goal of the research presented in this paper was to study a new software paradigm – adaptive software – in which the structure of an adaptive program is patterned upon the structure of an adaptive controller. Towards this aim, we implemented a domain-specific (object/target recognition) program (A Reconfigurable Architecture for Adapting to Changes in the Requirements (RAACR)) that can adapt to changes in software requirements through the incorporation of feedback. RAACR is a hierarchy of domains (blackboards). Each domain includes multiple knowledge sources (KSs) and a domain scheduler (DS). In response to feedback, KSs change their processing parameters, while DSs change the scheduling policy of the KSs. A generic communication mechanism is implemented on the CORBA compliant SPRING operating system. The adaptability of the program is evaluated quantitatively using a requirements volatility measure and the probability of correct recognition.     

Ontology-based automated support for goal–use case model analysis

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. To ensure the quality of such modeled artifacts, a detailed model analysis needs to be performed. However, current requirements engineering approaches generally lack reliable support for automated analysis of consistency, correctness and completeness (3Cs problems) between and within goal models and use case models. In this paper, we present a goal–use case integration framework with tool support to automatically identify such 3Cs problems. Our new framework relies on the use of ontologies of domain knowledge and semantics and our goal–use case integration meta-model. Moreover, functional grammar is employed to enable the semiautomated transformation of natural language specifications into Manchester OWL Syntax for automated reasoning. The evaluation of our tool support shows that for representative example requirements, our approach achieves over 85 % soundness and completeness rates and detects more problems than the benchmark applications.     

Fluid in Video: Augmenting Real Video with Simulated Fluids

We present a technique for coupling simulated fluid phenomena that interact with real dynamic scenes captured as a binocular video sequence. We first process the binocular video sequence to obtain a complete 3D reconstruction of the scene, including velocity information. We use stereo for the visible parts of 3D geometry and surface completion to fill the missing regions. We then perform fluid simulation within a 3D domain that contains the object, enabling one‐way coupling from the video to the fluid. In order to maintain temporal consistency of the reconstructed scene and the animated fluid across frames, we develop a geometry tracking algorithm that combines optic flow and depth information with a novel technique for “velocity completion”. The velocity completion technique uses local rigidity constraints to hypothesize a motion field for the entire 3D shape, which is then used to propagate and filter the reconstructed shape over time. This approach not only generates smoothly varying geometry across time, but also simultaneously provides the necessary boundary conditions for one‐way coupling between the dynamic geometry and the simulated fluid. Finally, we employ a GPU based scheme for rendering the synthetic fluid in the real video, taking refraction and scene texture into account.     

Animating Volumetric Models

This paper describes a technique to animate three-dimensional sampled volumes. The technique gives the animator the ability to treat volumes as if they were standard polygonal models and to use all of the standard animation/motion capture tools on volumetric data. A volumetric skeleton is computed from a volumetric model using a multi-resolution thinning procedure. The volumetric skeleton is centered in the object and accurately represents the shape of the object. The thinning process is reversible in that the volumetric model can be reconstructed from the volumetric skeleton. The volumetric skeleton is then connected and imported into a standard graphics animation package for animation. The animated skeleton is used for reconstruction, which essentially recreates a deformed volume around the deformed skeleton. Polygons are never computed and the entire process remains in the volumetric domain. This technique is demonstrated on one of the most complex 3D datasets, the Visible Male, resulting in actual “human animation”.